round robin numerical flow simulation in wind energy - dewi

Francesco Durante, Volker Riedel

Ulrike Bunse, Peter Busche, Harald Mellinghoff, Dr. Kai Mönnich, Till Schorer

DEWI GmbH - Deutsches Windenergie-Institut

Wilhelmshaven, Germany

2008-03-08

This project was sponsored by the German Federal Ministry for the Environment, Nature Conservation

and Nuclear Safety, FKZ 0329965.

Round Robin

Numerical Flow Simulation in Wind Energy

Final Report

Round Robin Numerical Flow Simulation in Wind Energy

DEWI GmbH - Partial Reproduction not Permitted 2 / 173



Disclaimer

The following results refer to one single site with three met masts installed, they refer to the model in

the version and configuration used in the test and they refer to a specific model operator/team. Under

no circumstances can these results be considered as a general statement about the quality of the

service provided by each company, nor can these results be considered as a general quality assessment

of the computer model used. In particular, if a third party flow model is used by the participant, the

results cannot be considered as a statement of quality of the third party model. The reasons are as

follows:

1. Flow models in general have many parameters to set and simulation design decisions have to be

made, depending on the site under consideration and depending on the experience of the model

operator. It is not clear if the model operator during the Round Robin Test will be the same than

during normal business model operation.

2. Wind farm sites are very different, especially regarding orographic complexity. The results that

refer to this site are not transferable to any other site and are valid only for this specific site.

3. Measurement uncertainties specified below refer to the standard uncertainty, assuming a normal

distributed error. Although less likely, the actual deviation between measurement value and real

value can be higher than the standard error.



Table of Content

1 Preface ...........................................................................................................................................9

2 Sponsors .......................................................................................................................................10

3 Setup of the Round Robin test.......................................................................................................11

3.1 The aim...................................................................................................................................11

3.2 The choice of the test site .......................................................................................................11

3.3 The dummy Run......................................................................................................................12

4 Description of the Participating Models.........................................................................................13

5 Description of the Main Test .........................................................................................................20

5.1 Input Data-set.........................................................................................................................20

5.1.1 Elevation..........................................................................................................................21

5.1.2 Land Cover.......................................................................................................................23

5.1.3 Meteorological data.........................................................................................................25

5.1.4 Coordinates .....................................................................................................................28

5.2 Output Data-set ......................................................................................................................28

5.2.1 Mean Horizontal Wind Speed for terrain-following surfaces............................................28

5.2.2 Mean Wind Speed for the target locations .......................................................................29

5.2.3 Wind Statistics for target locations in 80 m above Ground Level ......................................29

6 Assessment of the Results.............................................................................................................30

6.1 Weibull A- and k-Parameter....................................................................................................30

6.2 Mean Horizontal Wind Speed for a terrain-following surface at different heights ...................33

6.3 General Remarks on the Evaluations Regarding Wind Speed...................................................43

6.3.1 Technical details on the assessment of the statistics ........................................................43

6.4 Important Remarks regarding the Results of the Participant ANM...........................................46

6.5 Incomplete Data Submitted by Participant UNJ.......................................................................49

6.6 Incomplete Data Submitted by Participant REP .......................................................................49

6.7 Evaluations Regarding Mean Wind Speed ...............................................................................50

6.7.1 Quantitative Evaluations with Respect to the Sector-wise Speed-Up Factors....................51

6.7.2 Deviations from all Participants Mean Values...................................................................54



6.7.3 Mean Absolute Values of Sector-wise Errors ....................................................................55

6.7.4 Overall Direction Independent Wind Speed Prediction Error ............................................56

6.8 Evaluations Regarding Mean Squared Wind Speed..................................................................67

6.9 Evaluations Regarding Mean Cubed Wind Speed ....................................................................77

6.10 Evaluations Regarding Wind Direction ....................................................................................86

6.10.1 Qualitative Evaluation Regarding Veer Angles ..................................................................86

6.10.2 Qualitative Evaluation Regarding Veer Angles: Target Mast 1...........................................86

6.10.3 Qualitative Evaluation Regarding Veer Angles: Target Mast 2...........................................87

6.10.4 Evaluations Regarding the Overall Wind Direction Distributions.......................................88

6.10.5 Assessment of Average Sector-Wise Wind Direction Errors ..............................................90

6.10.6 Assessment of the Directional Dispersion of the Single Sector Wind Direction Results .....90

6.11 Single Turbine AEP................................................................................................................104

6.12 Energy Yield Results: Single Turbine AEP ...............................................................................105

6.13 Wind Farm Energy Yield Results ............................................................................................113

7 Conclusions.................................................................................................................................121

8 Appendix ....................................................................................................................................125

8.1 Overview of the properties of the on-site measurements .....................................................125

8.1.1 Wind Direction Effects ...................................................................................................129

8.1.2 Speed-up Factors ...........................................................................................................132

8.1.3 Turbulence Intensity ......................................................................................................135

8.1.4 Sector-wise Wind Speed Correlation Properties .............................................................142

8.1.5 Filling of a Gap in Target Mast 1 Data .............................................................................148

8.1.6 Filling of a Gap in Target Mast 2 Data .............................................................................152

8.2 Issues related to the site selection process............................................................................156

8.2.1 General Considerations..................................................................................................156

8.2.2 Wind Speed Measurement ............................................................................................157

8.2.3 Spatial Arrangement ......................................................................................................158

8.2.4 Wind Direction Measurements ......................................................................................158

8.2.5 Exemplary illustrations of some problematic effects found during site search................161

8.2.6 Maps .............................................................................................................................166



8.2.7 Forest ............................................................................................................................166

8.2.8 Coordinates ...................................................................................................................167

8.2.9 Data from Numerical Site Calibrations ...........................................................................168

8.3 Output data Format..............................................................................................................170

8.3.1 Mean Horizontal Wind Speed for terrain following surfaces - Data Format.....................170

8.3.2 Mean Wind Speed for target locations - Data Format.....................................................170

8.3.3 Wind Statistics for target locations - Data Format ..........................................................171

9 References..................................................................................................................................173



1 Preface

Wind energy has successfully developed world wide during the last years. Along with this good

development, there have been continuous improvements of the tools used for the simulation of the

wind flow conditions. Future installations are foreseen to concentrate over off-shore areas and to

develop more and more over internal complex terrain sites. With the exploitation of wind energy over

internal areas, the complexity of the surface and flow conditions makes the requirements for a proper

wind resource assessment and for a correct definition of the financing risks even higher. Despite a

general improvement of several aspects of resource assessment methods (such as the analysis of the

anemometers effects, the long term correlation techniques etc. ), the methods for extrapolation of

wind conditions from a measurement mast to a wind turbine site has remained more or less unchanged

since the early nineties: The European Wind Atlas is a „de facto“ standard method for most of the

energy yield calculations. Limitations and weaknesses of this methodology are well known and under

complex terrain conditions (or other non-standard situations) large systematic errors are to be

expected. An alternative solution to improve the reliability of resource assessment over complex

orography is represented by the use of fluid dynamical numerical models. Presently, different

typologies of numerical models and methodologies are adopted for wind resource evaluation. Most of

them have been initially developed for purposes marginally related to wind energy applications such as

automotive industry or weather forecasting. While in other sectors of research and engineering the

numerical solution of the fluid dynamic equations has become a standard approach for the derivation

of the flow properties, in wind energy the adoption of numerical flow models as an accepted standard

method still lacks the necessary experience and still requires a systematic classification and a scientific

evaluation of errors and of the related uncertainties. Such an evaluation would support decisions for a

wide range of wind energy actors like banks, financing companies, developers, etc. It is believed that

the presence of a scientific classification and an independent assessment of the myriad of different

wind simulation methods will eventually improve the acceptability of the concept of numerical

simulation on the whole. This project aims, as much as possible, to fill the aforementioned gaps.



2 Sponsors

This research was sponsored by the following parties:

- German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety

- Bremer Landesbank

- Commerzbank

- Deutsche Immobilien Leasing GmbH

- GE Energy

- HSH Nordbank AG

- Nord/LB

- Ostwind Verw.ges.

- WPD Beteiligungs GmbH

- DEWI GmbH



3 Setup of the Round Robin test

3.1 The aim

Numerical models are currently widely used in wind energy analysis to extrapolate the wind conditions

from a point where wind conditions are measured to other points of a wind farm area where wind

conditions are unknown. Despite a certain number of numerical models do exist which do not use on-

site wind measurements at all, the extrapolation of punctual measurements is the primary use of

numerical models in micrositing, site assessment and energy yield analysis. Accordingly, the Round

Robin test aims to systematically evaluate the capabilities of the considered models of extrapolating

wind measurements.

Therefore the participants were requested to perform a simulation which takes as input wind

measurements at one reference point of the site, (along with topographic and climatic data) and

calculate wind conditions for two other target points for which wind measurements are available but

unknown to the participants.

3.2 The choice of the test site

DEWI has a database of many hundred sites for which wind measurements have been carried out.

Many of the measurements of this database are directly performed by DEWI according to the IEC

standard (IEC 61400-12-1, IEC 61400-12), MEASNET and the IEA recommendations, both for wind

potential studies and for power performance measurements including site calibrations. The remaining

sites are equipped with third-party wind measurements and mainly used in previous wind potential

studies executed world-wide by DEWI during the last years. In order to set-up a proper model

verification, a certain number of requirements should be satisfied. These requirements refer both to

the site characteristics and to the properties of the measurement equipment. Table 1 shows a list of

minimum requirements that should be fulfilled by the pair „site/measurement equipment”. During the

selection of a site suitable for the main test it turned out that the difficulty of this task was by far

underestimated. A list of examples of problems encountered during the selection of the site is reported

in detail in Appendix 8.2. The by-product of this selection process represents by itself, in our point of

view, an unexpected, new and valuable result. Having investigated a large number of complex terrain



wind measurements from the last years and different sources, we put forward the hypothesis that in

general the measurement errors in complex terrain are underestimated, even if the equipment fully

conforms to the standards. This is especially true for the wind direction measurement, whose high

importance is not enough recognized. We were not fully aware of the meaning of these difficulties for

the Round Robin test and, accordingly, we underestimated by far the effort for site selection.

Requirement Relevance Condition satified in

the main test

More than one high quality measurement mast present at the site. High Yes

Data collected at least for wind speed and wind direction for a reasonably long common period (at least

several months, 1 year desirable).

High Yes

Distance between in the order of a few kilometers Medium Yes

Availability of a detailed documentation of the measurement equipment. High Yes

The measurement heights should be at least 30m. Preferably at least one measurement should be at hub

height of current modern wind turbines.

Medium Yes

Anemometers have to be individually calibrated (preferably MEASNET or DAP approved) and should

preferably be of type Thies First Class, Risoe or Vector.

High Yes

The site should present strong complex features (steep slopes, flow channeling, flow separation). Medium No

The wind distribution at the target points should not be easily obtainable with „standard” techniques (e.g.

WAsP)

Medium Unknown

The site must be free of operating wind turbines. High Yes

The wind data-set must not be public available, particularly, wind data should not have been given

previously to any of the participants.

High Yes

Ownership issues. The owner of the data set must agree to the use of the data in the test. High Yes

Table 1: List of requirements to be fulfilled by the couple „site/measurement equipment”.

3.3 The dummy Run

The DEWI Round Robin Test on Numerical Flow Simulation started with a test of the test procedure

itself, which was called „Dummy-Run”. This test was designed to identify possible problems in the data

transfer between DEWI and the participants and vice versa. In particular, attention was paid in order to

ensure that the data that is submitted to DEWI by the participants was delivered in the correct data

format. During this test it was also possible to ensure that the participants are provided with all the

information needed to run their model. The whole „Dummy-Run” was therefore designed in a similar

way as the final run and it was useful for the participants to get confidence with the test and practice



with several data and respective data formats. From the point of view of today, the dummy-run was

very useful and successful and without the dummy-run the main test may have failed.

4 Description of the Participating Models

In total, 8 participants took part in the Round Robin test. All of them used different models. The

participants were from Germany (6), France (1) and Japan (1). A list of names of participants, address

and contact person is reported in Table 2.

It should be noted that the participant CeBeNetwork GmbH did not manage to perform the requested

simulations. Therefore it was not possible to evaluate this participant within this test. Nevertheless

CeBeNetwork has expressed his wish to actively take part in the project in future in case of further

tests.

During the preparation phase of the Round Robin test, the participants were asked to fill in a

questionnaire collecting information about the applied models. The main goal of this questionnaire was

to help DEWI in designing the Round Robin test regarding the required input and output data and its

format for the test. Table 3 summarizes the information returned by the participants about the

technical description of their models. Throughout the report, the participant's contributions are

referred to by their shorthand symbols, that are specified on top of Table 3 .

Besides the contributions of the 8 participants, 2 further („virtual“) participants are added and shown in

all results, where applicable. The first of those has the shorthand symbol „MES” and represents the

actually measured data at the target masts, to that the participants contributions are compared. This

data was not made available to the participants during the test. The second „virtual“ participant has the

shorthand symbol „SPL“, which stands for the „simple“ method, which uses the reference mast data as

it is, without any transformation or modification, i.e. the „SPL” method neglects any horizontal or

vertical variation of the wind conditions in the area in question, therefore representing the reference

mast data.



Istitution/Company Address Contact Person

Anemos

Gesellschaft fuer Umweltmeteorologie mbH

Bunsenstrasse 8

D-21365 Adendorf, Germany

H.-T. Mengelkamp, Sven Huneke

CeBeNetwork GmbH Gewerbepark ATP

Hein-Saß-Weg 36

D-21129 Hamburg

René Volkert

GEO-NET Umweltconsulting GmbH Große Pfahlstraße 5

D-30161 Hannover, Germany

Peter Trute, Ingo Wendt

Lahmeyer International GmbH Friedberger Str. 173

D-61118 Bad Vilbel, Germany

Volodimir Kremenetskiy, Oliver Heil

Meteodyn 75 Bd, Alexandre Oyon

72100 LE MANS

Aurélien Chantelot

RWTÜV SYSTEMS GmbH Langemarckstraße 20 45141 Essen - Germany H.O.Wulf

Fachhochschule KIEL , FB Maschinenwesen Grenzstrasse 3 D-24149 Kiel - Germany Prof. Dr. Alois Schaffarczyk

Kyushu University Department of Mechanical Engineering Science,

Kyushu University

744 Moto-oka, Nishi-ku, Fukuoka, 819-0395 JAPAN

Prof. Hikaru Matsumiya

Repower Systems AG Alsterkrugchaussee 378

D-22335 Hamburg

Tomas Blodau-Konick

Table 2: List of participants, respective addresses and contact persons.



Anemos GmbH Geo-Net

GmbH

Lahmeyer

International

GmbH

Meteodyn Tüv Nord Uni-Kiel Kyushu-University REpower

Shorthand

Symbol

ANM GEO LAM MET RTV UNK UNJ REP

Name and version

of the used model

MM5 + METRAS FITNAH KLIMM_32 Meteodyn WT

2.0

Austal2000 FLUENT 5 LES Windsim

Developer of the

model

MM5: Pennsylvania

Atate

University/NCAR

Metras: Univ. HH

G. Gross,

Hannover

Institute of

Atmospheric

Physics,

University of

Mainz, Lahmeyer

International

METEODYN Ingenieurbüro

Janicke, Dunum

FLUENT Inc. Kyushu Univercity,

CRC Research

Institute, Inc.

Name of

underlying model

and version

MM5: MM5V3.7

METRAS: Version

1.0

not specified Version 3.2 Meteodyn WT

2.0

not specified Fluent not specified not specified

Developer of the

underlying model

MM5: Pennsylvania

State University /

National Center for

Atmospheric

Research, Metras:

Univ. HH

not specified not used METEODYN not specified not specified not specified not specified

References on the

model

MM5:

http://www.mmm.u

car.edu/mm5/

Metras: Schlünzen,

K.H., Bigalke K.,

Lüpkes C. &

Niemeier U. von

Salzen K. (1996):

Concept and

realization of the

mesoscale

transport- and fluid-

model METRAS.

Meteorologisches

Institut Universität

Hamburg. METRAS

Techn. Report, 5, pp

156

e.g.

Meteorol.Ztsc

hr.,2002, 295-

302;

J.Geophys.Re

s. 1998, 7875-

7886,

Numerical

simulation of

canopy flows,

Springer

Verlag 1993

Beitr. Phys.

Atmosph.,

November 1997,

p.301-317, A

Three-

Dimensional

Viscous

Topography

Mesoscale

Model

www.meteodyn.

com ; „For a

reliable wind

assessment

whatever the

terrain“,

D,Delaunay,

poster EWEC 04.

„Wind field

evaluaiton in

complex terrain

for transport

safety

applications“,

D.Delaunay,

ERCOFTAC 04.

not specified not specified Kogaki,

T.,Kobayashi, T.,

Taniguchi, N.,

1999,

„Conservative

Finite Difference

Schemes for

Incompressible

Turbulent Flow in

Generalized

Coordinates”,

Turbulence and

Shear Flow

Phenomena -1:

First International

Symposium, Santa

Barbara,

California.

not specified

Table 3 (Part 1 of 5): Model description as reported by the participants.



Anemos GmbH Geo-Net

GmbH

Lahmeyer

International

GmbH


Shorthand Symbol ANM GEO LAM MET RTV UNK UNJ REP

Field Variables used

by the model

MM5: all

Mtetras: all

Wind vector,

temperature,

humidity, pressure,

turbulent kinetic

energy, soil

variables

(temperature,

water)

Wind Vector V,

Temperature

gradient, Pressure

P, Turbulent Kinetic

Energy

Wind vector V,

Pressure P,

Turbulente Kinetic

Energy

not specified Wind Vector V,

Potential

Temperature T

Pressure P,

Turbulent Kinetic

Energy TKE, TKE-

Dissipation-Rate

EPS, ...

Wind Vector V,

Pressure P

not specified

Does your model use

the hydrostatic

approximation?

MM5: no

Metras: no

No No No not specified not specified No not specified

Does your model

treat the

atmosphere as

compressible?

MM5: No

Metras:: no

No No not specified not specified yes, possible No not specified

Does your model

describe the effect of

the Coriolis force?

MM5: Yes

Metras:: Yes

Yes Yes No not specified yes, possible No not specified

Space discretization MM5: finite

differences

Metras: finite

differences

Finite differences Finite volumes Finite Volume not specified FV Finite differences not specified

General Type of Grid MM5: Cartesian

Metras: Cartesian

Variable

(horizontal), terrain

followinf (vertical)

Cartesian Structured grid with

vertical and

horizontal

refinement

not specified unstructured BFC Structured/Boundary

Fitted

not specified

Structured grid

topology

MM5: nested if

requied

Metras: nested if

requied

Nested grids Single-Block,

Rendering

Single-Block not specified unstructured BFC Single-Block not specified

Unstructured grid

cell type

0 0 - 0 not specified tets, hex, pyramids

prims

not specified

Variable horizontal

grid spacing?

MM5: Yes

Metras: Yes

Yes Yes Yes not specified Yes, possible Yes not specified

Typical vertical

grid spacing [m]

(If variable please

specify the range)

MM5: variable to

be decided

Metras: variable

to be decided

5m near the

ground 500 m at

top (typically

6000-8000 m)

20 m up to 1000

m, if required

5m at the ground

to 200m at the

top

not specified variable

depending on

topography

Variable not specified




Anemos GmbH Geo-Net

GmbH

Lahmeyer

International

GmbH



Minimum usable

horizontal grid

spacing in complex

terrain [m] (estimate)

MM5: 1km

Metras: 50m

1 m for airflow

around individual

obstacles (e.g.

buildings) , 50-100

m for wind energy

studies

20 m up to 200 m,

typically 50 m to

100 m grid cell

resolution is

applied

5m 30m 1/1000 of

computational

horizontal

extension

0.5m not specified

Typical number of

horizontal grid points

(if appropriate)

MM5: to be

decided

Metras: to be

decided

200 x 200 typically 400 * 400

* 40 -> 6.400.000

depending on

hardware/processi

ng

220x220 not specified 150 x 150 200x300 not specified

Typical number of

grid points in the

vertical direction (if

appropriate)

MM5: to be

decided

Metras: to be

decided

30-50 typically 30 to 50

vertical cells

40 not specified 50 70 not specified

Is the structured grid

smoothed, e.g. by

Laplace-Type or

Poisson-Type

smoothing

operations?

MM5: no

Metras: no

no if converging

problems appear,

the grid can be

smoothed with

several methods

other not specified laplace (ICEM/CFD) Poisson not specified

Iteration type MM5 : impicit

Metras: impicit

explicit Implicit Implicit not specified both possible Semi implicit

(Adams-Bashforth

for convection

terms and Crank-

Nicolson for

diffusion terms)

not specified

Convection scheme(s)

used

MM5: Kain-

Fritsch 2

(includes

shallow

convection), tbd

Metras: tbd

centered

differencing

ADI - method UDS not specified 2nd order upwind

schema

second-order

upwind scheme

(QUICK Scheme)

not specified

Type of Wind

Velocity Vector

MM5: cartesian cartesian Cartesian not specified not specified cartesian at cell

centre

Cartesian not specified

Variable arrangement

and type of

horizontal staggering

MM5: B-grid

staggering

Metras:

Arakawa C grid

Arakawa C equidistant in x and

y - direction

Colocated not specified co-located

(probably)

Colocated not specified

How do you state

that your

simulation has

converged? (if it

applies)

residuals Mean

differences in a

5 min interval

falls below

threshold

Maximum

change in

variable falls

below threshold

Residuals fall

below a

threshold

not specified residuals falls

three orders of

magnitude,

changing of

special varables

Residuals fall

below a

threshold

not specified




Anemos GmbH Geo-Net

GmbH

Lahmeyer

International

GmbH



Sub-grid turbulence

parametrization

(turbulence closure)

MM5: tke based

MY 2 .5 closure

Metras: MY level

2.5

TKE 1st order scheme,

TKE based

TKE Based not specified standart k-epsilon Large eddy

simulation or

Pseudo large eddy

simulation

not specified

Type of k-epsilon

model (if applies)

not specified not specified None not specified not specified launder-spalding,

spalard-allmeras

not specified not specified

Planetary

Boundary Layer

parameterization

(If any)

MM5: Eta PBL

(predicts TKE

and has local

vertical mixing),

tke

Metras: tke

not specified None M.O not specified None not specified not specified

Land-surface model

(if any)

MM5: 5-layer

soil model, tbd

Metras: tbd

FITNAH (surface)

(e.g. Meteorol

Rdsch. 1991, 97-

112)

Viscosity - model not specified not specified None not specified not specified

Radiation

parametrization

(if any)

MM5: Cloud-

radiation

scheme

Metras: tbd

FITNAH (radiation)

(e.g. Meteorol.

Rdsch. 1991, 97-

112)

None not specified not specified None not specified not specified

Other

parametrizations

MM5: Cumulus:

New Kain-Fritsch

Metras: tbd

cloud microphysics,

urban canopy,

forest canopy

None not specified not specified None not specified not specified

Time dependent

simulation with

time dependent

boundary

conditions or

steady state

simulations?

MM5: Time

dependent

simulation with

time dependent

boundary

conditions

Metras: both

probably

steady state

simulation

Steady state

simulation

Steady State

simulation

not specified steady state

prefered, transient

possible but CPU-

time consuming

Time dependent

simulation with

fixed boundary

condition

not specified

Do you use

domain nesting?

One-way or two-

way? How many

domains?

MM5: depends

on the area

Metras: depends

on the area

nesting with 4

domains

Nesting No nesting not specified do you mean

multi-griding ?

Yes not specified




Anemos GmbH Geo-Net

GmbH

Lahmeyer

International

GmbH



Typical calculation

time to obtain

information on wind

conditions in a wind

farm in complex

terrain (about

1km2). If you run

time dependent

simulations,

consider a period of

one year.

MM5: 1-2 month

Metras: 1-2 month

1-3 days Typically 20 m

resolution with 50

x 50 x 40 cells

require 2 days for

stationary

condition on a

modern PC

One day not

specified

2-4 hours of CPU

time for one run

(time step)

Several years with

finest scale

analysis

not specified




5 Description of the Main Test

The Dummy Run was a useful step to identify issues in the data requirement and to improve the data

flow between DEWI and the participants. Since all major issues in the data requirement and in the data

flow were clarified, it was possible to use the same data-set type and the same structure as used for the

„Dummy Run“. The participants already had, in some way, familiarity with this data-set type and the

respective format. Similarly, the variables requested as output are basically of the same kind and with

the same format as those requested during the Dummy Run.

5.1 Input Data-set

The input data for the test was a set of gridded and punctual data describing elevation, land cover and

meteorological situation for an area close to the site (about 8 km around the point of interest) and for

the mesoscale area surrounding the site (for an area extending approximately 200 km around the

location of interest). The aim was to provide the relevant input data for both microscale and mesoscale

models. The participant was free to choose the data that he requires among those that are provided by

DEWI. The use of other data outside this data-set was not allowed. Information about the source and

format of the input data-set is provided in Table 4.

Variable Format File name

Elevation – fine scale ASCII Surfer Grid data/elevation/fine/elevation-fine.grd.zip

Elevation – large scale Geotiff data/elevation/large/elevation-large.tif.zip

Land cover – fine scale ASCII Surfer Grid data/landcover/fine/landcover-fine.grd

Roughness Lenght - fine scale ASCII Surfer Grid data/landcover/fine/roughness-fine.grd

Land cover- large scale Geotiff Data/landcover/large/landcover-large.tif.zip

Wind conditions close to the site MS Excel file Data/meteo/winddata/winddata.xls

Reanalysis data NetCDF Data/reanalysis/XXX.200X.nc.zip

Upper soundings ASCII Data/soundings/sound.zip

Reference mast coordinates MS Excel file Data/coordinates/reference.xls

Target coordinates MS Excel file Data/coordinates/target.xls

Table 4: Summary of the input data for the main test



5.1.1 Elevation

Two data-sets were provided:

- a high resolution digital elevation model for the area close to site;

- a coarser-resolution digital elevation model for the mesoscale area around the site.

5.1.1.1 Elevation close to the site

This data, derived by topographic maps edited by Istituto Geografico Militare (IGM), describes the

terrain elevation of an area extending about 16.5 km in the South-North direction and 18 km in the

West-East direction. This area is approximately centred on the location of the reference mast. The grid

spacing is equal to 10 m. Values of elevation are in meters. The coordinates were given in UTM zone 32,

the datum was according to WGS84. The format was ASCII Surfer grid.

H:\gruppe\micrositing\Forschung\RoundRobinSimulation\doc\final_report_tables_figures\cutted\

Figure 1: Fine scale elevation data-set used by the participants. The position of the three measurement masts used

for the assessment of the models is reported.



5.1.1.2 Elevation ~ 200 km from the site

A second elevation data-set, derived from the Shuttle Radar Topography Mission (SRTM), describes the

terrain elevation for a rectangular area extending approximately 500x400 km. The grid spacing is

0.0008333° (~90 m). Elevation values are expressed in meters above the mean sea level. The

coordinates are given in the geographical latitude-longitude coordinate system with the ellipsoid

according to the WGS84 datum. Provided format was GeoTiff.



5.1.2 Land Cover

Two data-sets of land cover at two different resolutions were provided to the participants. Both data-

set are derived by the EU „CORINE land cover 2000 vector by countries” data-set and checked against

photographs taken during a site visit and topographic maps for the area close to the measuring masts.

The land cover data-set was given to derive values of roughness length or whatever other property

required by the model (evaporation, albedo etc.). The participants were asked to assign values for this

properties according to their usual practice. Among these variables, the roughness length usually plays

an important role in fluid-dynamic simulations and together with elevation represent the only two

variable needed to describe the lower boundary conditions for most of the microscale models used in

wind energy. For this reason, for the area close to the site, the participants were directly provided with

an additional data-set of roughness length. Since the derivation of the roughness length from the

observation of land cover properties is in itself a somehow arbitrary process (and, above all, models

may use different roughness length definitions), the participants were asked to check, and possibly

modify, the roughness values against the corresponding land use categories.

5.1.2.1 Land cover close to the site

This data-set describes land cover for an area extending about 16.5 km in the SN direction and 18 km in

the WE direction. The area is approximately centred on the location of the reference mast. The grid

spacing of this data-set is equal to 10 m. The coordinates system is UTM, zone 32, ellipsoid is according

to WSG84. Provided format is Surfer ASCII grid. A table which relates each land cover category to the

respective codes was given as separate MsExcel file.



Figure 2: Fine scale land use data-set used by the participants. The position of the three measurement masts used for

the assessment of the models is reported.

5.1.2.2 Roughness length close to the site

Values of roughness length according to the European Wind Atlas classification are given in the file

roughness-fine.grd. These data describe the surface roughness length for an area extending about 12.5

km in the South-North direction and 14 km in the West-East direction. This area is approximately

centred on the location of the reference mast. The grid spacing is equal to 10 m. The coordinates

system is UTM, zone 32, ellipsoid is according to WSG84. Provided format is Surfer ASCII grid. For the

reasons mentioned in Section 5.1.2, the participants were encouraged to check and possibly modify

these values according to the respective land cover categories.



5.1.2.3 Land cover ~ 200km from the site

A second land cover data-set, derived from the EU CORINE database, classifies terrain land cover for a

rectangular area extending approximately 500x400 km.

The grid spacing of this data-set is 0.0008333° (~90m). The coordinates were given as geographical

latitude longitude, Coordinate system and ellipsoid are according to WGS84 datum. Format is Geo-Tiff.

A table which relates each land cover category to the respective codes was given as separate MsExcel

file.

5.1.3 Meteorological data

Three sets of meteorological data were provided:

1. Wind measurements from a cup anemometer and wind vane at the site;

2. Upper soundings for one locations in the same mesoscale domain;

3. Global reanalysis data.

A description of each data-set is given in the following.

5.1.3.1 Wind measurements at the site (file WindData.xls)

The participants received wind measurements at the site for the reference point x=XXXXX, y=XXXXX

(coordinates in UTM WGS 84, Zone 32). Wind speed was measured at 43 m above ground level by a

Thies „First Class” cup anemometer, that was calibrated according to MEASNET standard. The wind

direction sensor (a „Thies compact” wind vane) measured wind direction at a height of about 42 m

above ground. The sampling frequency was 1 Hz. The averaging interval is 10 minutes. The data spans

the period 09/11/2005 – 30/06/2006. All data showing wind direction in the range [83° - 172°] have

been excluded from the data-set in order to minimize the effect of the mast top tube on the

measurements. For this reason several gaps affect the data-set. The wind speed time series is provided

as MS-Excel file. A photograph of the measuring mast is reported in Figure 3. As can be noticed, an

additional anemometer, of the type Thies „Classic“ is mounted on the top of the vertical boom. Many

studies have enlighten that the outdoor dynamic behaviors of the anemometers of different types can

lead to difference in the measured wind speed of some percent. Since all anemometers mounted on

the two target masts are of the type „Thies First Class“ we decide not to use the top anemometer as

reference (despite its better position) and to prefer, on the contrary, the use of the boom-mounted



„Thies First Class“ anemometer. In this way it has been possible to perform the comparison with an

homogeneous set of sensors. In order to separate as much as possible the evaluation of the wind

speed and wind direction and to obtain a more complete information on the performance of the

models, DEWI additionally provided 24 time series, one for each 15° direction sector. Each time series

contains only data with wind direction falling in a certain sector, therefore the data had several gaps.

The participants were asked to calculate the wind conditions at the target positions for each wind

direction sector (more details are given in Section 5.2).

Manufacturer and Type of Sensor Height above ground level Direction of the boom

Thies clima 4.3350.00.000 43 m 309°

Thies clima 4.3129.00.012 42 m 209°

Table 5: properties of the two sensors mounted on the reference mast.

Figure 3: Detail of the sensors mounted on the top section of the measuring mast (left) and picture of the entire

measuring system (right).



5.1.3.2 Upper sounding

A set of 6 hourly sounding reports for the period from October 2005 to November 2006 was provided

for the location Cagliari (9.05° W, 39.25° N). The soundings were not continuous, therefore gaps (even

of several days) occur in the time series. The following atmospheric variables were measured:

- Air pressure

- Height a.s.l.

- Air temperature

- Dew Point

- Relative humidity

- Mixing ratio

- Wind direction

- Wind speed (knots!)

- Potential temperature

- Equivalent potential temperature

- Virtual potential temperature

Each file contains upper-air reports for one month. Data were provided as ASCII files.

5.1.3.3 Global Reanalysis

Data from the „50 years NCEP/NCAR Reanalysis Project” were delivered for year 2005 and 2006 for the

following variables:

- U (zonal) component of the wind

- V (meridian) component of the wind

- Relative humidity

- Air temperature

- Geopotential Height

- Sea level pressure

- Skin temperature

All data, except skin temperature, were given on a regular grid with a resolution of 2.5° with a temporal

resolution of 6 hours. Skin temperature was given on a Gaussian grid. Longitude points for skin

temperature are listed in the self-describing file. The coverage for all variables was global. The grids for



u,v, relative humidity and air temperature have values on 17 vertical pressure levels. Grids for Sea Level

Pressure and Skin Temperature are two-dimensional. The reanalysis data-set has been provided to the

participants as NetCDF files.

5.1.4 Coordinates

The coordinates of the reference mast and the coordinates of the target points (i.e. points for which

wind mean wind speed and wind statistics shall be calculated) were given to the participants as two

MsExcel files.

5.2 Output Data-set

Participants were asked to submit results regarding the following quantities:

- Mean Horizontal Wind Speed for three terrain following surfaces (40m, 60m and 80m above

Ground Level);

- Mean Wind Speed for two target locations at 40m, 60m and 80m above Ground Level;

- Wind Statistics for two target locations at 80 m. Wind statistics „per sectors”.

All results were required to be calculated for the time periods for which values in the input wind data

file are available. Described differently: The results (mean, statistics, etc) should not comprise the wind

conditions occurring during periods of no data availability (gaps). Participants were instructed to not

correlate the on-site wind data with wind data from other sources to fill the gaps in this time series.

This condition is particularly important because the evaluation is bound to exactly those time steps that

are present in the on-site wind data time series. We would like to point out that the aim of the Round

Robin is to assess the capability of the models to transfer wind speed times series from one point to

another, correlation methods are not subject of this test. A description of the requested variables and

their respective format is given in the following sections.

5.2.1 Mean Horizontal Wind Speed for terrain-following surfaces

The participants were asked to calculate, for the time steps present in the input on-site wind data

(winddata_all.xls), the mean wind speed for three different heights (40m, 60m, 80m) above ground for

the following rectangular area (all coordinates in UTM WGS 84 Zone 32):

EAST: From X1 = XXXXX m to X2 = XXXXX m



NORTH: From Y1 = XXXXX m to Y2 = XXXXX m

The horizontal grid should consist therefore of 1067 (west-east) by 1098 (north-south) grid cells. The

required grid spacing was equal to 10 m. This grid spacing only refers to the format of the data to be

submitted to DEWI. Participants were allowed to perform the simulation at any desired resolution.

5.2.2 Mean Wind Speed for the target locations

For the time steps present in the input on-site wind data, the participants were asked to calculate the

mean wind speed at 40m, 60m, 80 above ground for the positions of the two target masts.

5.2.3 Wind Statistics for target locations in 80 m above Ground Level

With „Wind Statistics“ we refer to the percentage share of data that falls in a combined wind direction

and wind speed interval. For the time steps present in the input file winddata-all.xls, the participants

were asked to calculate the wind statistics for the location reported in the file target.xls at 80 m height

above ground level. In order to better separate wind direction effects, the participants were asked to

provide 24 separate directional wind statistics. Each directional wind statistics should describe the wind

conditions occurring during the periods contained in each of the 24 given time series at the target mast.

i.e. one statistics for the time steps contained in winddata_000.xls, one for the time steps contained in

winddata_015.xls, etc.



6 Assessment of the Results

6.1 Weibull A- and k-Parameter

DEWI has compared Weibull A and k parameters calculated from the predicted tab with parameters

obtained from observed wind data at the target masts. The Weibull fit was calculated according to the

method specified in the European Wind Atlas method. The measured and predicted parameters are

reported in Figure 4 (A (scale) parameter) and Figure 5 (k (shape) parameter).

No (relative) difference is calculated or shown. The actual differences in the k-parameter at the

reference and at the target masts are, in this case, approximately equal to 0.04. It can be noticed that

all models but „RTV” and „UNK” were under-predicting the scale parameter for both masts.

A very interesting result comes from the observation of the shape parameter. A large number of

models were basically reproducing the shape parameter of the reference mast („SPL” in the plot)

without improving the initial information. This can be seen as an hint that these models are affected by

difficulties in capturing horizontal and vertical variations of the shape parameter.

It is very difficult to find a single clear explanation for the errors found for the wind distribution. It can

be advanced the hypothesis that a certain number of participants have performed steady state

simulations with fixed boundary conditions only for a single wind speed. This hypothesis cannot be

confirmed at the present stage without collecting further information on the way the participants

applied their models and on the applied post-processing methods.

The present evaluation focuses on the ability of the considered models to properly predict energy

yields. Nevertheless it is worthwhile to recall here the importance of a reliable prediction of the wind

speed distribution for the estimation of the extreme winds. It is true that for a proper extreme wind

assessment a linear scaling of the wind conditions can lead to a wrong prediction of the actual intensity

of the extreme winds in complex terrain. The participant “GEO”, for instance, under-predicted by far

the shape parameter, with the difference between modelled and measured k parameter approximately

equal to 0.28. As mentioned above, this kind of underestimation might have important effects if the

calculations would be used as basis for the standards methods of estimation of the extreme winds.



5.5

6

6.5

7

7.5

5.5 6 6.5 7 7.5

Wei

bull

A at

Tar

get

Mas

t 2 [

m/s

]

Weibull A at Target Mast 1 [m/s]

Weibull scale parameter A

6.8,6.0 ANM

5.8,6.2 GEO

6.0,6.5 LAM

6.8,6.6 MES

6.3,6.2 MET

5.9,5.8 REP

7.3,7.2 RTV

5.6,5.6 SPL

6.4,6.3 UNJ

6.2,6.6 UNK

Figure 4: Weibull scale parameter.



1.5

1.6

1.7

1.8

1.9

2

1.5 1.6 1.7 1.8 1.9 2

Wei

bull

k at

Tar

get

Mas

t 2 [

-]

Weibull k at Target Mast 1 [-]

Weibull shape parameter k

1.68,1.79 ANM

1.58,1.57 GEO

1.89,1.92 LAM

1.85,1.84 MES

1.79,1.80 MET

1.78,1.77 REP

1.80,1.80 RTV

1.81,1.81 SPL

1.80,1.79 UNJ

1.67,1.70 UNK

Figure 5: Weibull shape parameter.



6.2 Mean Horizontal Wind Speed for a terrain-following surface at different heights

Separately for three terrain following surfaces (40 m, 60 m, 80 m) the participants calculated the wind

conditions for an area of 1067 (west-east) by 1098 (north-south) grid cells.

DEWI has averaged the wind speed maps provided by the participants point by point to three average

wind speed maps. Afterwards, for each participant and height level, the relative deviation of the map

from the averaged map is calculated and afterwards displayed together with the mean map.

The calculated mean of all models at the points of the target masts in 80 m is equal to 5.79 m/s for

target mast 1 and 5.78 m/s for target mast 2. These values differs from the measured wind speed of

about 5% for target mast 1 and about 3% for target mast 2. Since there is no way to compare these

results with measurements over all points of the domain, the following considerations will have mainly

a qualitative nature. Nevertheless this comparison represents a useful diagnostic for a better

understanding of results of the tests described in the previous and following sections.

Figure 6 shows the point-by-point mean of all participants for three different heights. Figure 7 to Figure

13 reports the mean wind speed (left side) and relative deviation from the all mean (right side) for all

participants. The relative deviation from the mean (RDM in the following) at different height can give

useful information about the vertical variation of the wind conditions of each model. For instance a low

RDM at 40 m and a high RDM at 80 can be a signal for a too high wind shear, that, in turn, can be the

effect of a too high atmospheric stability and/or too high roughness length. It can be argued that this

was the case for participant „RTV”. This can be also an explanation for the overestimation of the wind

conditions by this participant in the remaining tests. The other participants seem to approximately

maintain constant RDM at different heights.

Another information can be derived by the observation of the grid resolution. Participants delivered

information about the typical resolution of their model before the beginning of the test (see Table 3). It

is not certainly known at which resolution the participants executed the simulation for this specific test.

Anyway some of the considered models produce significant spatial differences in wind speed over

scales of few tens of meters. As an example, “UNJ” and “LAM” present relatively high horizontal

variation, especially for the north part of domain. For these participants, mean wind speed can vary, in

some areas, by 3% over distances of 30m. Such gradients are confirmed by our site calibration



measurements in complex terrain, where sometimes gradients of 15% are observed for distances of

180 m. It is clear that a model, which operates with a resolution coarser than about 20m to 30m, can

not sufficiently capture high variations. This seems to be the case for the participant “ANM” for which

the horizontal structure of the wind field suggests that a comparably coarse resolution was used.



Figure 6: Point-by-point mean of all participants. Wind speed at 40 m (above), 60 m (centre) and 80 m (below).



Figure 7: Participant „ANM”. Mean wind speed at 40 m (above, left), 60 m (centre left), 80 m (below left) and relative

difference from the mean of all models at 40 m , 60 m , 80 m (Right column).



Figure 8: Participant „GEO”. Mean wind speed at 40 m (above, left), 60 m (centre left), 80 m (below left) and relative




Figure 9: Participant „LAM”. Mean wind speed at 40 m (above, left), 60 m (centre left), 80 m (below left) and relative




Figure 10: Participant „MET”. Mean wind speed at 40 m (above, left), 60 m (centre left), 80 m (below left) and relative




Figure 11: Participant „RTV”. Mean wind speed at 40 m (above, left), 60 m (center left), 80 m (below left) and relative




Figure 12: Participant „UNJ”. Mean wind speed at 40 m (above, left), 60 m (center left), 80 m (below left) and relative




Figure 13: Participant „UNK”. Mean wind speed at 40 m (above, left), 60 m (center left), 80 m (below left) and relative

difference from the mean of all models at 40 m , 60 m , 80 m (Right).

6.3 General Remarks on the Evaluations Regarding Wind Speed

As detailed before and in the appendix, the common time series of the wind speed and direction data

of the reference mast and the two target masts is considered (common time steps). From this time

series, all data sets are excluded that show a wind direction at the reference mast in between 82.5

degree and 172.5 degree in order to minimise effects from the mast top tube on the wind speed sensor.

This time series is then subdivided into 15 degree wind directions sectors with respect to the wind

direction measured at the reference mast. DEWI provided the participants with the time series of wind

speed and direction at the reference mast (without the disturbed sector), in the form of 10 minute

average values of wind speed, direction, wind speed maximum, minimum and standard deviation for

each 10 minute interval. The participants were asked to transform this data towards the two target

masts and to submit the resulting wind statistics (TABs) for the two target masts to DEWI as their result.

Additionally, the participants were asked to transform towards the two target masts also the time

series that contain only data from within one 15 degree wind direction sector at a time and return to

DEWI the transformed data from each of those sectors as a result. The second step regarding the

sector-wise transformation enabled DEWI to carry out detailed wind direction dependent analyses that

allow a more detailed model assessment compared to the use of just one output statistics for all

undisturbed sectors.

6.3.1 Technical details on the assessment of the statistics

The quantitative evaluations of the wind statistics (TABs) was conducted as follows:

1. No sector-wise evaluations of the overall TAB were conducted (i.e. of the TAB that the participants

submitted as a result of their transformation of the time series containing all data except the

disturbed sector). The reason is that if we would compare this TAB to the measured one sector by

sector, we would mix model errors regarding wind speed with those that are related to wind

direction. Those mixing errors have significant influence on the sector-by-sector comparison but

will in a real wind resource assessment by far not play such a significant role. As a result, the overall



tab, as it is defined above, is only evaluated regarding the overall, wind direction independent wind

speed distribution and regarding the single turbine and farm energy yield that is calculated with

that statistics. For the comparison of the overall, direction independent values of mean wind speed,

mean squared wind speed and mean cubed wind speed, these values are calculated from the TABs

(wind statistics) in the following way: The relative share of data that falls into each wind speed

interval of 1 m/s width is multiplied by the average of the upper and lower wind speed class

boundaries (i.e. 0.5 m/s for the wind speed interval [0m/s, 1m/s[) and taken to the power of 1,2 or

3, depending on the type of assessment (mean wind, mean squared, mean cubed wind speed).

Resulting values are summed over all wind speed intervals containing data. Relative shares given in

the TAB files regarding wind direction and wind speed are normalised separately such that the

relative shares always sum to 1.

2. The mean, mean squared and mean cubed wind speed of the sector-wise statistics (i.e. those

statistics that are the result of the transformation of the reference mast data that is reduced to one

15 degree wind direction sector at a time) is also evaluated according to the summing procedure as

detailed above.

3. As a result, for the evaluations regarding wind speed (squared, cubed wind speed), the statistics

(TABs) from the participants are evaluated only regarding the wind speed. The fact that the data in

the wind statistics may be slightly dispersed over different sectors even for the „single sector TABs“

has no influence on the results. This dispersion is considered in the assessments regarding wind

direction.

4. The participants results are compared to the measured data. The measured mean (sector-wise and

overall) wind speed, mean squared and mean cubed wind speed is also calculated by first creating a

statistics (TAB) and afterwards summing up the statistics as detailed above. An alternative way

would be to calculate the measured values directly from the time series, without using an

intermediate statistics. However, if a participant would submit exactly the correct measured

statistics, he should get assigned a relative deviation from the measured data of 0.0%, i.e. an exact

match. If we would compare participants results to values obtained directly from the time series,

this exact match would not be guaranteed, due to the wind speed resolution of 1m/s of the

statistics. For the test, we considered the use of TABs also for the measured data as most

appropriate, because otherwise we should have asked the participants to provide time series for



the target masts instead of statistics. But most models do by default output only statistics and not

time series. As the participants provide statistics with a resolution of 1m/s, we find it reasonable to

compare that to the values obtained from such a statistics also.

The participants were asked to submit result statistics (TABs) for the target mast. In addition, they were

asked to submit to DEWI mean wind speeds for the positions of the two target masts for the height

levels 40m, 60m and 80m. A comparison of the different submissions is shown in Table 6.

It can be observed that for participants ANM, MET, REP and UNJ there are no significant differences

between the mean wind speed that they submitted as a number and the mean wind speed that DEWI

calculated from the statistics that the participants submitted for the target masts. For GEO, LAM, RTV

and UNK there are significant deviations: The mean wind speed that they submitted as a number is

higher than the mean wind speed that DEWI calculated from the submitted statistics (up to 7%).

This shows once again that there are different methods for the calculation of mean wind speed. It may

be calculated from the time series directly, it may be calculated from a statistics or it may be calculated

from Weibull parameters that are fitted in some way to the statistics. It is not known to DEWI, how the

participants calculated the mean wind speed. In order to maintain overall consistency, we decided to

use for the main comparison between the models regarding mean wind speed only the mean wind

speed that we calculated from the submitted statistics, because otherwise we would possibly compare

mean wind speeds calculated with different methods and because we consider the mean wind speed

calculated from the statistics as an appropriate measure.



Partici-

pant ID

Target Mast

1 (80m)

Target Mast

2 (80m)

Target

Mast 1

(40m)

Target

Mast 1

(60m)

Target

Mast 1

(80m)

Target

Mast 2

(40m)

Target

Mast 2

(60m)

Target

Mast 2

(80m)

Target

Mast 1

(80m)

Target

Mast 2

(80m)

Target

Mast 1

(80m)

Target

Mast 2

(80m)

Target

Mast 1

(80m)

Target

Mast 2

(80m)

ANM 6.04 5.31 5.31 5.77 6.04 4.57 5.04 5.31 100% 100% 99% 89% 99% 89%

GEO 5.38 5.87 4.68 5.04 5.33 5.06 5.44 5.74 99% 98% 87% 96% 87% 96%

LAM 5.38 5.82 4.96 5.32 5.68 5.13 5.58 5.97 106% 103% 93% 100% 93% 100%

MET 5.69 5.59 5.04 5.40 5.68 5.03 5.34 5.59 100% 100% 93% 93% 93% 94%

REP 5.29 5.22 n.s. n.s. 5.29 n.s. n.s. 5.23 100% 100% 87% 87% 87% 88%

RTV 6.55 6.48 5.16 6.17 6.87 4.84 5.82 6.54 105% 101% 113% 109% 113% 110%

UNJ 5.79 5.69 4.95 5.50 5.78 4.94 5.43 5.69 100% 100% 95% 95% 95% 96%

UNK 5.55 5.88 4.96 5.42 5.76 5.64 6.01 6.30 104% 107% 94% 105% 94% 106%

n.s. : value not submitted

Target mast 1, mean wind speed calculated from the measured time series: 6.10 m/s

Target mast 2, mean wind speed calculated from the measured time series: 5.95 m/s

Target mast 1, mean wind speed calculated from the measured wind statistics: 6.11 m/s

Target mast 2, mean wind speed calculated from the measured wind statistics: 5.98 m/s

Ratio of wind speed, submitted as a number, to ...

Mean Wind Speed,

calculated by DEWI from

Target Mast Statistics

submitted by Participants

[m/s]

Mean Wind Speed, submitted to DEWI as a

Number [m/s]

...wind speed as

calculated by DEWI

from the submitted

Statistics [%]

...measured wind

speed as calculated

by DEWI from

Statistics [%]

...measured wind

speed as calculated

by DEWI from the

time series [%]

Table 6: Comparison of submissions regarding mean wind speed.

6.4 Important Remarks regarding the Results of the Participant ANM

Looking at the wind data analysis (see appendix) and the data that the participants finally submitted as

their results, this direction dependent evaluation strategy must be considered as fully appropriate, with

the exception of the results of the participant ANM. As an example consider the wind direction

distribution shown in Figure 14. This wind direction distribution refers to target point 1 and is the result

of the participant's transformation of the wind data from within the 15 degree wind direction sector

centred at north at the reference mast. Similar but in general less pronounced situations are observed

for the other directions. It can be observed that besides the peak in the distribution for the second

sector centred at 15 degree, there is a secondary peak for wind from west. Other wind directions are

also present with a small relative share. There is no indication that the secondary peak at west and the

overall small relative share of data in all sectors corresponds to the measured data (Figure 15).

It is likely that the participant ANM did not obtain the target mast results by transforming the data sets

provided by DEWI. The target mast data may be obtained directly by other means (e.g. mesoscale time

dependent simulation) and then somehow filtered to get the sector-wise results. This strategy differs

from the strategy that is most likely applied by all the other participants. This other strategy aims at

obtaining and applying information on a transformation function between reference mast and the

target masts with the help of flow models. In the simplest case, this function may be a direction



dependent wind speed scaling factor and a wind speed offset, but other, more sophisticated functional

relationships are possible (depending on wind speed / nonlinear, depending on other external

parameters).

Evaluations in the following chapters will give some information on the performance of the different

strategies for this site. However, the sector-wise evaluations with the wind direction sectors defined at

the reference mast and the „same time criterion“ that is used to obtain the suitable target mast data

does not fit very well to the evaluation of the sector-wise data of the participant ANM. Especially the

evaluations regarding wind direction effects in later chapters are of questionable applicability to the

sector-wise data of participant ANM. In particular, it is reasonable to calculate a mean wind direction

for the distribution in Figure 15 but a mean wind direction has no meaning for the distribution shown in

Figure 14).

The sector independent results of participant ANM (the result of the transformation of all the data) may

also be affected by this problem due to the gaps in the data (excluded sector). As all participants agreed

to the test evaluation procedure (including the sector-wise assessment) and expect us to carry out the

assessment in the way specified, we decided to assess also the sector-wise data of the participant ANM

in exactly the same way than the data of the others, with special remarks where necessary. Finally, we

are still convinced that our proposed sector-wise evaluation method is a suitable method to assess the

participants results, although the applicability to the data of participant ANM is questionable. The

sector-wise assessment regarding wind direction sectors defined at the reference mast is the method

that is also used in similar form in a site calibration in the context of a IEC power curve measurement.



N

W E

S

Figure 14: Wind direction distribution submitted by participant ANM for target mast 1 (reference mast wind direction

sector centred at 0 degree (north).

N

W E

S

Figure 15: Measured wind direction distribution for target mast 1 (reference mast wind direction sector centred at 0

degree (north).



6.5 Incomplete Data Submitted by Participant UNJ

The participant UNJ did not submit to DEWI the sector-wise results, i.e. the results of the

transformation of the time series that contained only one 15 degree wind direction sector at a time.

This is a severe restriction on the assessment of the results of this participant and as a result, no

complete comparison is possible between the results of UNJ and the other results.

From a practical point of view, we decided not to exclude participant UNJ from the test, although this

might be considered as unfair by other participants. It is mentioned in the conclusions that no full

comparison is possible regarding the results from UNJ. Nevertheless, the other results from participant

UNJ can be compared to those of the other participants.

We hope that the other participants agree to this procedure. Participant UNJ is located in Japan. In

particular, unfortunately there was a special general holiday in Japan during the time that the

participants had for the preparation of the results. We did not know that in advance and we were then

not able to consider that due to the already present project delays. The participant had in some way to

cope with that and we are happy that he did take part.

6.6 Incomplete Data Submitted by Participant REP

The participant REP did not submit complete results.

Regarding the sector-wise results, REP did only submit the tabs containing transformed data of the 15

degree sectors centred at 180 degree and 270 degree. REP provided these tabs for each of the two

target masts. With the exception of the sector-wise results the submitted data of REP is complete.

We decided to include the sector-wise results of REP wherever possible. Possible further tests in future

must have restrictive contracts that state how the case of partial result submission is handled. Clearly,

in any average taken over sector-wise results (e.g. mean absolute values of sector-wise errors), REP is

excluded. Only in the analysis of the sector-wise deviations of the participants results from the sector-

wise mean of the participants results is REP included.

The results of REP are not directly comparable to the results of the other participants, which is

mentioned in the conclusions.

In the sector-wise plots, the results of participant REP appear in the form of two dots and not as lines,

as with the other participants.



6.7 Evaluations Regarding Mean Wind Speed

Accurate calculation of mean wind speed is mainly important for site classification according to the IEC

wind turbine classes.

Figure 16 and Figure 17 report the sector-wise mean wind speeds for target mast 1 and 2, respectively,

as calculated by the participants along with the measured mean wind speed and the results of the

„virtual“ participant SPL, who neglects any horizontal and vertical variation in the wind conditions and

therefore represents the measured data from the reference mast.

At target mast 1 and 2 there is a general trend towards higher mean wind speeds for southerly winds

and increasingly lower wind speeds towards north.

All participants follow this general trend. It cannot be stated if participant REP also follows that trend

due to the fact that he only submitted results for two sectors.

There is relatively large variation among the participants sector-wise mean wind speed results: At

target point 1 the largest variation within a sector can be found at 15 degree, where participants results

vary between 3.1m/s and 6m/s, whereas the measured mean wind speed in this sector amounts to

4.2m/s. This means that the participants results range from to an underestimation of -25.7% to an

overestimation of 43.4%, which must be considered as a extremely large range of results.

Figure 18 and Figure 19 report the sector-wise percentage errors in mean wind speed for target mast 1

and 2 respectively. At target mast 1 (Figure 18), the result of MET (+43.3% error) must be considered as

an outliner, regarding the results of the other participants in this sector and regarding the results of

MET for the neighbouring sectors 0 degree and 30 degree.

Participant RTV is the only participant who overestimates sector-wise mean wind speed in all sectors at

target mast 1. With the exception of the sectors 0 degree and 15 degree, participant RTV shows for

each sector the highest overestimation.

The most pronounced underestimation of mean sector-wise wind speed at target mast 1 shows

participant GEO (sector 330 degree, -32.3% underestimation).

The situation regarding deviations from measured values is different at target mast 2 (Figure 19). As

detailed in the appendix, there is a pronounced increase of the speed-up factors regarding target mast

2 and the reference mast for the wind direction range 195 to 315 degree.



For the wind direction sector centred at 15 deg, there is peak-like behaviour not only for MET (as for

target mast 1) but also for UNK.

In general, the models tend to underestimate wind speed for the range 330 deg to 75 deg. For the wind

direction range 180 deg to 330 deg, overall model performances are dominated by the fact that most of

them they fail to reproduce the strong change of the ratio of wind speeds measured at target mast 2 to

those at the reference mast. The fact that participants LAM and UNK provide an approximation of the

change of this ratio with wind direction (for the wind direction range 180 deg to 330 deg) confirms that

it has a meteorological reason and is not a measurement error.

6.7.1 Quantitative Evaluations with Respect to the Sector-wise Speed-Up Factors

Within the analysis of the measured data from the site (see appendix), a pronounced directional

pattern regarding the sector-wise speed-up factors for target mast 2 was found. Therefore it is checked

in how far the results of the participants show this pattern. The comparison is done only in a qualitative

way in order to assess if the models are in general able to reproduce these important site

characteristics. Quantitative evaluations are not conducted regarding the speed-up factors because

they have no direct meaning in terms of wind resource assessment and the ultimate goal of the

modelling efforts is not to calculate accurate speed-up factors, but to calculate accurate energy yields.

Additionally, the speed-up factors only capture simple affine sector relationships and do not account

for non-linear or otherwise determined functions.

Figure 20 and Figure 21 show the measured and simulated speed-up factors for target mast 1 and 2

respectively. Note that the measured speed-up factors are derived from the sector-wise wind statistics

due to the reasons explained in section 6.3. Therefore these speed-up factors differ slightly from those

reported in the measurement data evaluation in the appendix, where the speed-up factors were

derived from the time series. The difference is negligible for the present qualitative assessment.

6.7.1.1 Speed-up Factors at Target Mast 2

Overall, there is little similarity between measured and calculated speed-up factors, which is a

disappointing results. Only the speed-up factors calculated from the results of LAM at target mast 2 and



of ANM at target mast 1 show some similarity to the measured ones for all wind directions. In those

two cases can one assume that the models capture site specific wind flow characteristics.

For smaller wind direction ranges, many models show similarity of the calculated to the measured

speed-up factors, in particular UNK and MET (target mast 2; 180 deg to 240 deg), RTV (target mast 2;

315, 330, 345 deg) and UNK (target mast 1; 315 to 345 deg).

Regarding target mast 2 and the wind direction range 195 deg to 315 degree, measured data shows a

pronounce increase of the speed-up factor with increasing wind direction (clockwise turning).

Qualitatively, this increase is reasonably modelled only by the participant UNK, although UNK predicts

decreasing speed-up factors for wind directions greater than 300 deg.

For the sectors 180 deg up to 240 deg, the shape of the speed-up factor function is reproduced very

well by the participants UNK and MET. MET predicts increasing speed-up factors up to wind directions

of 255 deg, which fully conforms to the measured ones. Results of ANM fit very well the minimum

speed-up factor at 195 deg. To some degree also LAM reproduces the general shape of the speed-up

factor function for the wind direction range 180 deg to 345 deg, although details are visible that are not

present in the data.

Although participant ANM matches the minimum speed-up factor quite well, he fails to reproduce the

overall increasing shape of the speed-up factor function. On the contrary, ANM predicts decreasing

speed-up factors for clockwise direction turning.

Results of the participant RTV differ completely from all the other results. RTV predicts no significant

change of the speed-up factor for all directions, which does not conform to the measured data. It

seems that RTV did mainly model an average vertical wind speed increase and did by far not enough

take into account site specific smaller scale wind flow effects.

Regarding the wind direction range from 0 deg to 75 deg, the unrealistic peaks in the sector-wise

predictions of MET and UNK become obvious. The general shape of the speed-up function for this wind

direction range is not reproduced by any of the participants.

Overall, for target mast 2 and all wind directions, the results of participant LAM provide, qualitatively,

some approximation of the variation of the speed-up factors with wind direction, although it seems

that details are modelled that are not present in the measured data. Participants MET and UNK model



the shape of the speed-up function for southerly and south-westerly winds nearly perfect, but show

large differences for other directions.

6.7.1.2 Speed-up Factors at Target Mast 1

Overall, the shape of the speed-up factor function versus wind direction is best reproduced in a

qualitative sense by participant ANM.

For the wind speed range 180 deg to 345 deg, participant UNK predicts the same overall shape of the

speed-up function than for target mast 2, although the measured function is completely different

(decreases for increasing wind directions). On the other hand, participant UNK provides the best fit to

the measured function in a qualitative sense for the directions 315 deg to 345 deg.

Regarding participant RTV, the predicted speed-up function does not change much at target mast 1

compared to target mast 2. There is hardly any change with wind direction.

Results of participant MET show a peak behaviour for the direction 15 deg, similar to the peak at the

same direction for target mast 2. On the other hand, for participant UNK the peak that was present at

target mast2 at 15 deg is not present at target mast 1.



6.7.2 Deviations from all Participants Mean Values

If one investigates the participants results without referring to the measured data at the target masts,

one can relate the sector-wise predicted mean wind speeds to the sector-wise mean wind speed of all

participants. This type of analysis is the base for the Round Robin tests conducted by MEASNET for

anemometer calibration, where one aims at maintaining consistent, comparable measurements and

does not refer to an externally measured value. This sector-wise „all-participants-mean“ is calculated

while taking care to not consider „SPL“ and „MES“ in the average and to consider that REP has provided

results only for two sectors. Results are shown in Figure 22 (target mast 1) and Figure 23 (target mast

2).

It can be noticed that for target mast 1 and the sector centred at 345 deg, the participants results can e

divided into a centre cluster, close to the mean value of all participants, and one outliner towards large

positive deviations from the mean (RTV) and one outliner towards large negative deviations (GEO). The

situation is similar for 330 deg and also 315 deg, although at 315 deg UNK separates from the centre

cluster towards positive deviations from the mean.

Overall, RTV provides for most sectors the highest positive deviation from all participants.

The situation is different at target mast 2. RTV does not in all cases provide the highest positive

deviation, but especially for 285 deg to 330 deg, participant UNK delivers higher positive deviations.

UNK remains in the centre cluster of the participants for the wind direction range 180 deg to 240 deg

but with further increasing wind direction UNK moves towards higher positive deviations, leaving

behind RTV at 285 deg to 330 deg.

At 330 deg there appears to be an obvious discrepancy between predictions by LAM and GEO. For 15

deg there is peak-behaviour for MET and UNK.

As a result of the evaluation of the sector-wise deviations from the sector-wise mean of all participants,

one can state that there is considerable variation of these quantities from sector to sector, which would

complicate the definition of a Round Robin „passed“ criterion similar to MEASNET anemometer

calibration round robins. Also, outliners and peak behaviour weaken the meaning of a mean value

calculated from the participants results. And lastly, the models under consideration in this test are

inhomogeneous regarding their general approach and it may be necessary to average their results

within distinct groups.



6.7.3 Mean Absolute Values of Sector-wise Errors

As shown in the measurement data evaluation in the appendix, the relationship between the reference

mast and the two target masts changes much with direction, especially at target mast 2. There are

different demands on the models for different wind directions. If in each sector the absolute value of

the error in mean wind speed is considered and averaged over all wind directions, one yields an

average mean absolute wind speed error that is based on a larger, dispersed data set than the wind

speed error gained from all wind directions simultaneously. Therefore, we consider that the results

presented in Figure 24 provide the best condensed overview of the model performance regarding mean

wind speed for this site. Whether or not there is systematical bias in the predictions will be investigated

in more detail in the next chapter.

By chance, MET shows the same mean error for both target masts (11.1%). Also RTV and UNK show

similar magnitudes of the error for both masts. For the other participants, there are larger differences

in the errors regarding the two masts.

ANM shows the smallest error for target mast 1 from all participants but the largest from all for target

mast 2. That ANM shows the smallest error for target mast 1 confirms our qualitative statement that

ANM provides the best approximation of the overall shape of the direction dependent speed-up

function for target mast 1.

The same is true for LAM regarding target mast 2. LAM provided a good approximation of the sector-

wise speed-up values and, accordingly, shows the smallest error for target mast 2. However, at target

mast 2, LAM does not perform that much better than the others as it is observed for ANM at target

mast 2.

Surprisingly, RTV mixes well among the other participants, although it was shown in section 6.7.1 that

RTV does hardly predict any change of the speed-up factors with wind direction. This is an indication

that the other models may partly introduce flow details and complexity that is not present in reality.

It must be noted that these evaluation regarding mean wind speed have no direct meaning for energy

yield prognosis, where accuracy in higher moments of wind speed is more important due to the power

performance behaviour of the wind turbines. But in terms of site assessment according to IEC wind

turbine classes, accurate calculation of mean wind speed is very important.



6.7.4 Overall Direction Independent Wind Speed Prediction Error

As detailed in the previous section, a sector-wise evaluation provides a broader base for quantitative

conclusions about overall model performance for this site, except for participant ANM, as detailed in

section 6.4. For ANM, the sector-wise evaluation may lead to wrong conclusions about site specific

model performance.

In this section, the mean wind speed that the participants calculated for the target masts, is evaluated

independent of direction from the statistics (TAB) that contain all sectors with the exception of the

disturbed ones. This evaluation relates to the percentage error in mean wind speed and not to its

absolute value and therefore provides information about any bias present in the participants results.

Figure 25 provides on overview of the wind speed errors of the different models.

All participants, except RTV underestimate mean wind speed for both masts. RTV as the only

participant overestimates mean wind speed at both masts.

As in the sector-wise evaluation, RTV and MET have similar errors for both masts, in the present

analysis this is additionally true for REP.

ANM confirms its comparably small error for target mast 1, the same is true for LAM in the case of

target mast 2.



0

1

2

3

4

5

6

7

8

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n W

ind S

pee

d [

m/s

]

Wind Direction Sector Centre [deg]

Target Point 1 - Mean Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK

ANM GEO LAM MES MET REP RTV SPL UNJ UNK

0 3.9 2.6 4.0 3.4 3.3 - 3.9 3.0 - 3.1

15 4.5 3.1 4.6 4.2 6.0 - 4.6 3.5 - 3.6

30 5.3 4.9 5.5 5.8 6.0 - 6.3 4.9 - 5.1

45 4.6 4.1 4.7 4.8 4.8 - 5.4 4.2 - 4.2

60 5.0 5.0 4.0 4.7 4.9 - 5.5 4.2 - 4.3

75 5.2 5.5 4.8 5.0 5.6 - 6.3 4.9 - 5.0

180 7.7 6.8 6.9 7.8 6.9 6.4 7.8 6.0 - 6.4

195 6.8 6.1 6.0 6.7 5.8 - 7.0 5.4 - 5.5

210 7.1 6.3 5.9 7.1 6.0 - 7.3 5.6 - 5.8

225 7.2 7.0 5.9 7.4 5.9 - 7.7 5.9 - 6.4

240 6.2 5.4 4.3 6.0 4.7 - 6.2 4.7 - 5.2

255 6.0 5.7 5.4 6.2 5.6 - 6.4 4.9 - 5.7

270 5.9 5.7 5.0 6.2 5.6 5.4 6.6 5.0 - 6.1

285 6.4 5.5 5.6 6.9 6.5 - 7.3 5.7 - 7.0

300 6.0 4.9 5.6 6.1 5.6 - 6.8 5.3 - 6.5

315 5.6 4.6 5.5 6.0 5.4 - 6.3 4.9 - 5.9

330 5.1 3.8 5.0 5.7 5.4 - 6.1 4.8 - 5.5

345 4.3 3.1 4.1 4.2 4.3 - 4.8 3.8 - 4.1

Figure 16



0

1

2

3

4

5

6

7

8

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n W

ind S

pee

d [

m/s

]


Target Point 2 - Mean Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 3.5 3.5 4.2 4.0 3.1 - 3.9 3.0 - 3.4

15 4.2 4.1 4.5 4.8 5.7 - 4.6 3.5 - 5.4

30 5.1 5.9 6.0 6.5 5.2 - 6.2 4.9 - 5.4

45 4.5 5.0 5.5 5.7 4.2 - 5.3 4.2 - 4.5

60 4.9 5.5 4.9 5.6 4.2 - 5.4 4.2 - 4.7

75 5.1 6.2 5.4 6.0 5.7 - 6.2 4.9 - 5.6

180 6.6 6.9 6.7 6.8 6.6 6.2 7.7 6.0 - 6.7

195 5.8 6.3 6.0 5.7 5.7 - 6.9 5.4 - 5.8

210 6.0 6.5 6.3 6.0 5.9 - 7.2 5.6 - 6.0

225 6.1 7.3 6.4 6.4 6.3 - 7.6 5.9 - 6.5

240 5.2 5.5 5.2 5.4 5.3 - 6.0 4.7 - 5.4

255 5.1 5.7 5.9 5.7 5.9 - 6.3 4.9 - 5.9

270 5.1 5.7 6.0 5.9 5.6 5.7 6.5 5.0 - 6.4

285 5.6 6.4 6.1 6.8 6.3 - 7.3 5.7 - 7.5

300 5.3 6.0 6.2 6.5 6.0 - 6.8 5.3 - 7.0

315 4.9 5.7 5.8 6.4 5.4 - 6.3 4.9 - 6.5

330 4.5 4.7 5.9 6.2 5.1 - 6.1 4.8 - 6.2

345 3.8 4.3 4.4 4.9 4.0 - 4.8 3.8 - 4.6

Figure 17



-40

-30

-20

-10

0

10

20

30

40

50

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean W

ind S

pee

d [

%]


Target Point 1 - Percentage Error in Mean Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 15.8 -24.5 16.1 0.0 -4.1 - 15.6 -10.7 - -8.9

15 7.8 -25.7 8.9 0.0 43.4 - 9.4 -15.3 - -13.9

30 -7.7 -15.6 -5.2 0.0 3.8 - 9.4 -15.8 - -12.5

45 -4.0 -13.9 -0.7 0.0 -0.2 - 13.0 -13.1 - -12.2

60 7.5 7.3 -14.6 0.0 5.3 - 18.0 -9.5 - -7.5

75 2.9 9.4 -5.1 0.0 11.1 - 25.6 -3.7 - -0.4

180 -0.4 -11.7 -10.8 0.0 -10.8 -17.9 0.8 -22.2 - -17.6

195 1.2 -8.8 -10.3 0.0 -13.2 - 3.4 -19.8 - -17.7

210 0.1 -10.3 -16.3 0.0 -15.2 - 3.3 -20.6 - -17.6

225 -2.3 -5.3 -19.9 0.0 -20.1 - 5.0 -19.7 - -13.8

240 2.1 -11.0 -28.1 0.0 -22.4 - 2.3 -21.6 - -13.6

255 -3.2 -7.2 -12.9 0.0 -9.8 - 4.4 -20.2 - -7.3

270 -4.8 -8.0 -19.3 0.0 -9.9 -12.6 6.4 -18.6 - -1.7

285 -7.0 -19.8 -19.3 0.0 -6.4 - 6.0 -18.1 - 1.7

300 -1.0 -19.5 -8.4 0.0 -8.1 - 12.4 -12.8 - 6.5

315 -6.2 -23.4 -7.4 0.0 -10.4 - 5.6 -17.3 - -1.7

330 -9.1 -32.3 -11.1 0.0 -4.7 - 8.7 -14.6 - -2.2

345 3.5 -25.3 -1.7 0.0 1.3 - 15.2 -10.5 - -2.8

Figure 18



-30

-20

-10

0

10

20

30

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean W

ind S

pee

d [

%]


Target Point 2 - Percentage Error in Mean Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 -11.6 -10.9 5.2 0.0 -22.5 - -1.3 -23.4 - -13.9

15 -13.2 -14.3 -5.9 0.0 17.9 - -5.2 -26.3 - 13.1

30 -22.4 -9.2 -8.4 0.0 -19.7 - -4.5 -25.4 - -16.5

45 -20.4 -12.0 -2.8 0.0 -26.8 - -6.9 -27.0 - -20.8

60 -12.2 -2.3 -12.7 0.0 -25.7 - -3.3 -24.4 - -16.4

75 -15.2 3.6 -10.0 0.0 -4.3 - 4.0 -18.7 - -6.6

180 -2.9 1.9 -1.7 0.0 -3.0 -8.9 14.1 -11.0 - -1.5

195 1.5 10.1 5.6 0.0 0.8 - 21.2 -5.2 - 1.0

210 0.0 8.1 5.0 0.0 -1.2 - 20.4 -6.1 - 0.1

225 -4.8 12.9 -0.7 0.0 -2.1 - 17.7 -8.0 - 1.0

240 -2.9 1.8 -3.7 0.0 -2.2 - 12.0 -12.4 - -0.7

255 -10.9 0.2 4.0 0.0 3.9 - 10.9 -13.7 - 4.3

270 -13.5 -4.1 1.5 0.0 -4.5 -2.8 10.8 -14.5 - 8.6

285 -17.3 -6.9 -11.4 0.0 -7.8 - 7.1 -17.2 - 9.8

300 -18.6 -7.9 -5.0 0.0 -7.5 - 5.5 -18.2 - 8.7

315 -23.1 -11.0 -8.3 0.0 -14.3 - -0.6 -22.2 - 2.9

330 -26.8 -24.1 -4.4 0.0 -17.2 - -1.0 -22.3 - 0.1

345 -22.0 -11.1 -9.2 0.0 -17.8 - -0.9 -23.0 - -6.5

Figure 19



0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 1

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 1Participant ANM

ANMMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360v

(T

arg

et M

ast

1)

/ v

(R

efer

ence

Mas

t) [

-]Wind Direction Sector Centre [deg]

Speedup Factor at Target Mast 1Participant GEO

GEOMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 1

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 1Participant LAM

LAMMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 1)

/ v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 1Participant MET

METMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 1

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 1Participant RTV

RTVMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 1

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 1Participant UNK

UNKMeasured

Figure 20: Speed-up Factors for Target Mast 1 versus Reference Mast.



0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 2)

/ v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant ANM

ANMMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360v (

Tar

get

Mas

t 2)

/ v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant GEO

GEOMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 2

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant LAM

LAMMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 2

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant MET

METMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 2

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant RTV

RTVMeasured

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 30 60 90 120 150 180 210 240 270 300 330 360

v (

Tar

get

Mas

t 2

) /

v (

Ref

eren

ce M

ast)

[-]


Speedup Factor at Target Mast 2Participant UNK

UNKMeasured

Figure 21: Speed-up Factors for Target Mast 2 versus Reference Mast.



-30

-20

-10

0

10

20

30

40

50

0 30 60 90 120 150 180 210 240 270 300 330 360Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean W

ind S

pee

d [

%]


Target Point 1 - Deviation from all Participants Mean Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 13.9 -25.8 14.2 0.0 -5.7 - 13.7 -12.2 - -10.4

15 2.7 -29.3 3.8 0.0 36.6 - 4.3 -19.4 - -18.0

30 -3.2 -11.5 -0.6 0.0 8.8 - 14.7 -11.7 - -8.2

45 -1.0 -11.2 2.3 0.0 2.9 - 16.5 -10.4 - -9.5

60 4.7 4.5 -16.8 0.0 2.6 - 14.9 -11.9 - -9.9

75 -4.1 2.0 -11.5 0.0 3.6 - 17.1 -10.2 - -7.1

180 10.3 -2.1 -1.2 0.0 -1.1 -9.0 11.7 -13.8 - -8.6

195 9.4 -1.3 -3.0 0.0 -6.0 - 11.9 -13.2 - -11.0

210 10.4 -1.1 -7.7 0.0 -6.5 - 13.9 -12.4 - -9.1

225 7.9 4.5 -11.6 0.0 -11.8 - 15.8 -11.4 - -4.8

240 15.7 0.9 -18.6 0.0 -12.0 - 16.0 -11.1 - -2.1

255 3.0 -1.3 -7.3 0.0 -4.1 - 11.1 -15.0 - -1.3

270 2.5 -0.9 -13.1 0.0 -3.0 -5.9 14.6 -12.4 - 5.8

285 0.6 -13.3 -12.8 0.0 1.1 - 14.5 -11.5 - 9.9

300 2.0 -17.0 -5.6 0.0 -5.3 - 15.9 -10.1 - 9.8

315 1.2 -17.4 -0.2 0.0 -3.4 - 13.9 -10.9 - 6.0

330 -0.8 -26.1 -2.8 0.0 4.1 - 18.7 -6.8 - 6.9

345 5.2 -24.1 -0.1 0.0 2.9 - 17.2 -9.0 - -1.2

Figure 22



-30

-20

-10

0

10

20

30

0 30 60 90 120 150 180 210 240 270 300 330 360Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean W

ind S

pee

d [

%]


Target Point 2 - Deviation from all Participants Mean Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 -2.7 -1.9 15.8 0.0 -14.7 - 8.7 -15.6 - -5.2

15 -12.1 -13.2 -4.7 0.0 19.4 - -4.0 -25.4 - 14.6

30 -10.4 4.9 5.8 0.0 -7.2 - 10.4 -13.8 - -3.5

45 -6.4 3.5 14.3 0.0 -14.0 - 9.4 -14.2 - -6.8

60 -0.1 11.2 -0.6 0.0 -15.5 - 10.0 -14.0 - -4.9

75 -11.0 8.8 -5.5 0.0 0.5 - 9.2 -14.6 - -2.0

180 -2.6 2.2 -1.4 0.0 -2.7 -8.6 14.4 -10.7 - -1.2

195 -4.9 3.2 -1.0 0.0 -5.5 - 13.6 -11.1 - -5.4

210 -5.1 2.6 -0.4 0.0 -6.3 - 14.3 -10.9 - -5.1

225 -8.5 8.6 -4.6 0.0 -5.8 - 13.2 -11.6 - -2.9

240 -3.6 1.1 -4.4 0.0 -2.9 - 11.3 -13.0 - -1.4

255 -12.7 -1.8 1.9 0.0 1.8 - 8.6 -15.4 - 2.2

270 -13.0 -3.6 2.0 0.0 -3.9 -2.2 11.4 -14.0 - 9.2

285 -13.5 -2.6 -7.3 0.0 -3.6 - 12.1 -13.4 - 14.8

300 -15.1 -3.9 -0.9 0.0 -3.5 - 10.1 -14.7 - 13.4

315 -15.4 -2.2 0.9 0.0 -5.8 - 9.3 -14.4 - 13.1

330 -16.6 -13.5 9.0 0.0 -5.7 - 12.7 -11.5 - 14.0

345 -12.1 0.2 2.3 0.0 -7.4 - 11.7 -13.3 - 5.4

Figure 23



0

5

10

15

20

0 5 10 15 20

Mea

n A

bso

lute

Err

or

in

Mea

n S

ecto

rwis

e W

ind S

pee

d

at

Tar

get

Mas

t 2 [

%]

Mean Absolute Error in Mean Sectorwise Wind Speed

at Target Mast 1 [%]

4.8,13.3 ANM

ANM

15.5,8.5 GEO

GEO

12.0,5.9 LAM

LAM

0.0,0.0 MES

MES

11.1,11.1 MET

MET

9.1,8.2 RTV

RTV

15.8,17.7 SPL

SPL

8.9,7.3 UNK

UNK

Figure 24



-20

-15

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

Err

or

in M

ean W

ind S

pee

d

at

Tar

get

Mas

t 2 [

%]

Error in Mean Wind Speed at Target Mast 1 [%]

-1.0,-11.2 ANM

ANM

-11.9,-1.9 GEO

GEO

-11.8,-2.7 LAM

LAM

0.0,0.0 MES

MES

-6.9,-6.5 MET

MET-13.3,-12.7 REP

REP

+7.2,+8.3 RTV

RTV

-17.4,-15.7 SPLSPL

-5.2,-4.9 UNJ

UNJ

-9.1,-1.6 UNK

UNK

Figure 25



6.8 Evaluations Regarding Mean Squared Wind Speed

While the assessment regarding mean wind speed is important especially for IEC wind turbine class

assessment and the assessment regarding mean cubed wind speed represents model performance

regarding calculation of the energy content of the wind, the importance of an assessment regarding

mean squared wind speed is not obvious.

However, if one aims at checking the model errors regarding power production of wind turbines, the

error in the mean squared wind speed provides a fairly good measure for that. It is in particular more

suitable than the evaluations regarding mean or mean cubed wind speed. On the one hand, errors in

mean wind speed will lead to much higher errors in power production. On the other hand, using the

measure that is in proportion to the energy content, mean cubed wind speed, one yields errors that are

in general too large because turbines can only extract part of the wind energy and because there is no

sensitivity of the power output on the wind speed if the turbine operates well at rated power.

As a result, the errors in mean squared wind speed, in particular the sector-wise evaluations, are of

central importance in the round robin test. Figure 26 to Figure 31 provide an overview of the sector-

wise results, similar to the evaluations regarding mean wind speed in the previous chapter. Compared

to that evaluation, the relative errors are larger but besides that the overall comparison of the single

sector results between the models leads to the same results. Therefore Figure 26 to Figure 31 are only

reported for reference.

The main result of the mean squared wind speed evaluation is shown in Figure 32 (mean sector-wise

error) and Figure 33 (mean absolute value of sector-wise error).

There is hardly any difference between model results of RTV and MET regarding the mean absolute

error in squared wind speed, whereas for the mean absolute error in mean wind speed, RTV performed

significantly better than MET.

ANM showed significantly lower errors regarding mean wind speed (sector-wise absolute values) than

any other model (at target mast 1).

Regarding mean absolute values of errors in squared wind speed, ANM shares the lowest errors with

UNK for target mast 1, which means that the accuracy of the UNK results is higher for squared wind

speed than for wind speed. In general, the differences between the mean and mean squared evaluation

are more pronounced in the case of target mast 1.



The overall picture regarding systematical bias (Figure 32) changes only little compared to the same

evaluation regarding mean wind speed. While all models except RTV underestimated mean wind speed

for both target masts, GEO and UNK slightly overestimate mean squared wind speed at target mast 2

and ANM slightly overestimates mean squared wind speed at target mast 1.

REP provides the most conservative results regarding mean squared wind speed for both target masts,

although ANM and LAM provide almost as conservative results for target mast 1 and 2 respectively.



0

10

20

30

40

50

60

70

80

90

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n S

quar

ed W

ind S

pee

d [

m2/s

2]


Target Point 1 - Mean Squared Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 21.8 8.7 18.3 14.4 13.2 - 19.1 11.3 - 12.5

15 28.4 13.2 25.2 22.5 45.4 - 26.5 15.8 - 17.4

30 37.8 34.2 38.8 44.9 49.0 - 54.0 31.8 - 36.5

45 30.8 24.9 30.3 30.5 31.4 - 39.7 23.3 - 25.5

60 40.1 36.0 22.7 29.5 34.8 - 43.4 25.3 - 28.0

75 43.6 41.3 31.3 35.3 43.5 - 55.0 32.2 - 35.5

180 85.5 58.0 57.0 75.3 58.8 49.9 74.9 44.4 - 51.0

195 67.2 48.9 45.9 58.7 44.7 - 62.9 37.7 - 41.2

210 68.7 52.0 44.5 63.7 47.1 - 69.3 40.8 - 45.8

225 67.3 64.4 44.5 68.3 45.4 - 77.5 45.2 - 54.3

240 49.4 37.4 24.4 44.3 28.9 - 49.5 28.9 - 37.4

255 46.3 43.3 35.9 46.4 40.5 - 53.7 31.4 - 45.0

270 42.7 40.4 31.6 45.6 39.2 36.8 54.1 31.5 - 48.7

285 49.0 40.1 38.0 55.8 51.6 - 65.8 39.1 - 63.3

300 44.4 31.8 37.5 44.5 39.4 - 58.7 35.0 - 55.1

315 40.1 28.8 37.8 45.3 37.2 - 51.2 31.3 - 46.4

330 34.2 20.1 31.9 42.0 38.5 - 49.5 30.5 - 42.3

345 26.0 13.8 21.5 23.9 24.5 - 31.3 18.8 - 23.9

Figure 26



0

10

20

30

40

50

60

70

80

90

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n S

quar

ed W

ind S

pee

d [

m2/s

2]


Target Point 2 - Mean Squared Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 16.9 14.9 20.4 19.0 11.7 - 18.8 11.3 - 15.3

15 23.9 21.3 24.7 28.8 40.5 - 26.2 15.8 - 42.4

30 32.7 48.9 45.4 55.4 37.3 - 52.3 31.8 - 42.4

45 28.1 35.0 40.3 41.5 24.1 - 38.2 23.3 - 29.5

60 35.4 44.3 33.6 42.5 25.1 - 41.7 25.3 - 33.1

75 36.9 55.2 39.4 47.5 45.9 - 52.9 32.2 - 44.1

180 59.2 59.7 53.1 56.6 53.1 46.7 73.1 44.4 - 55.1

195 46.5 51.9 45.5 42.8 43.0 - 61.8 37.7 - 43.8

210 48.1 55.3 49.7 47.7 45.7 - 67.3 40.8 - 47.9

225 48.1 70.9 51.9 53.7 51.8 - 74.1 45.2 - 56.4

240 35.5 39.7 34.0 36.8 36.5 - 47.6 28.9 - 39.3

255 33.4 43.1 43.9 41.5 45.9 - 51.7 31.4 - 48.1

270 31.9 39.6 44.1 42.6 39.9 41.2 53.4 31.5 - 53.2

285 37.6 49.6 45.0 56.4 49.0 - 65.8 39.1 - 71.4

300 34.1 44.3 46.4 51.5 45.3 - 58.7 35.0 - 64.7

315 30.2 41.2 42.4 49.1 38.4 - 51.2 31.3 - 57.2

330 26.5 31.2 44.5 47.4 34.9 - 49.5 30.5 - 53.3

345 19.9 24.8 25.9 30.4 21.8 - 31.3 18.8 - 30.0

Figure 27



-60

-40

-20

0

20

40

60

80

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean S

quar

ed W

ind S

pee

d [

%]


Target Point 1 - Percentage Error in Mean Squared Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 51.0 -39.8 27.2 0.0 -8.2 - 32.4 -21.5 - -13.0

15 26.1 -41.5 12.1 0.0 101.7 - 17.8 -29.9 - -22.7

30 -15.8 -23.8 -13.4 0.0 9.2 - 20.3 -29.1 - -18.8

45 0.9 -18.2 -0.6 0.0 3.0 - 30.4 -23.7 - -16.2

60 35.9 22.0 -23.1 0.0 17.8 - 47.0 -14.3 - -5.4

75 23.2 16.9 -11.6 0.0 23.1 - 55.6 -8.9 - 0.3

180 13.6 -22.9 -24.2 0.0 -21.9 -33.7 -0.5 -41.0 - -32.2

195 14.4 -16.7 -21.9 0.0 -23.9 - 7.1 -35.9 - -29.8

210 7.9 -18.4 -30.2 0.0 -26.0 - 8.8 -36.0 - -28.0

225 -1.5 -5.8 -34.9 0.0 -33.5 - 13.4 -33.9 - -20.6

240 11.7 -15.5 -44.9 0.0 -34.7 - 11.9 -34.7 - -15.5

255 -0.4 -6.7 -22.7 0.0 -12.8 - 15.7 -32.3 - -3.0

270 -6.4 -11.4 -30.8 0.0 -14.1 -19.4 18.7 -31.0 - 6.8

285 -12.1 -28.1 -31.9 0.0 -7.5 - 18.1 -29.9 - 13.5

300 -0.3 -28.5 -15.7 0.0 -11.5 - 31.8 -21.3 - 23.8

315 -11.4 -36.4 -16.5 0.0 -17.9 - 13.0 -30.9 - 2.4

330 -18.5 -52.3 -24.0 0.0 -8.3 - 17.9 -27.5 - 0.6

345 8.6 -42.2 -10.3 0.0 2.3 - 30.8 -21.5 - 0.0

Figure 28



-60

-40

-20

0

20

40

60

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean S

quar

ed W

ind S

pee

d [

%]


Target Point 2 - Percentage Error in Mean Squared Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 -10.6 -21.3 7.8 0.0 -38.2 - -0.7 -40.3 - -19.5

15 -17.2 -26.0 -14.2 0.0 40.4 - -9.2 -45.3 - 47.0

30 -41.1 -11.8 -18.1 0.0 -32.7 - -5.7 -42.6 - -23.6

45 -32.3 -15.5 -2.9 0.0 -41.9 - -7.9 -43.9 - -28.8

60 -16.7 4.5 -20.8 0.0 -40.9 - -1.7 -40.4 - -22.1

75 -22.3 16.3 -17.1 0.0 -3.4 - 11.5 -32.1 - -7.1

180 4.6 5.4 -6.1 0.0 -6.1 -17.5 29.2 -21.5 - -2.7

195 8.6 21.2 6.3 0.0 0.5 - 44.3 -12.0 - 2.3

210 0.9 16.0 4.1 0.0 -4.3 - 41.1 -14.6 - 0.3

225 -10.4 32.0 -3.3 0.0 -3.5 - 38.0 -15.8 - 5.1

240 -3.6 7.8 -7.6 0.0 -0.8 - 29.3 -21.5 - 6.6

255 -19.4 4.0 5.9 0.0 10.7 - 24.8 -24.2 - 16.0

270 -25.2 -7.1 3.3 0.0 -6.3 -3.4 25.2 -26.2 - 24.8

285 -33.3 -12.1 -20.3 0.0 -13.1 - 16.7 -30.7 - 26.5

300 -33.8 -13.9 -9.9 0.0 -12.0 - 14.1 -31.9 - 25.7

315 -38.6 -16.1 -13.7 0.0 -21.8 - 4.2 -36.2 - 16.5

330 -44.2 -34.3 -6.2 0.0 -26.3 - 4.4 -35.7 - 12.4

345 -34.4 -18.3 -14.8 0.0 -28.2 - 3.1 -38.1 - -1.1

Figure 29



-60

-40

-20

0

20

40

60

80

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean S

quar

ed W

ind S

pee

d [

%]


Target Point 1 - Deviation from all Participants Mean Squared Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 39.5 -44.4 17.5 0.0 -15.2 - 22.3 -27.5 - -19.7

15 9.1 -49.4 -3.0 0.0 74.5 - 1.9 -39.4 - -33.1

30 -9.4 -18.1 -6.9 0.0 17.5 - 29.4 -23.7 - -12.6

45 1.1 -18.1 -0.5 0.0 3.1 - 30.5 -23.6 - -16.1

60 17.4 5.4 -33.5 0.0 1.8 - 27.1 -25.9 - -18.2

75 4.5 -0.9 -25.0 0.0 4.4 - 31.9 -22.7 - -14.9

180 37.5 -6.6 -8.2 0.0 -5.4 -19.8 20.5 -28.6 - -17.9

195 29.7 -5.6 -11.5 0.0 -13.7 - 21.4 -27.3 - -20.4

210 25.9 -4.8 -18.5 0.0 -13.7 - 27.0 -25.3 - -16.0

225 14.3 9.3 -24.5 0.0 -22.8 - 31.5 -23.3 - -7.8

240 30.6 -1.1 -35.6 0.0 -23.7 - 30.9 -23.6 - -1.2

255 4.9 -1.8 -18.7 0.0 -8.2 - 21.8 -28.8 - 2.1

270 1.9 -3.6 -24.7 0.0 -6.6 -12.3 29.1 -24.9 - 16.2

285 -4.4 -21.9 -26.0 0.0 0.5 - 28.4 -23.8 - 23.4

300 -0.2 -28.5 -15.7 0.0 -11.4 - 31.9 -21.3 - 23.9

315 -0.3 -28.5 -6.0 0.0 -7.6 - 27.1 -22.2 - 15.3

330 -5.2 -44.4 -11.5 0.0 6.8 - 37.2 -15.5 - 17.1

345 10.6 -41.1 -8.7 0.0 4.2 - 33.2 -20.1 - 1.9

Figure 30



-60

-40

-20

0

20

40

60

0 30 60 90 120 150 180 210 240 270 300 330 360

Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean S

quar

ed W

ind S

pee

d [

%]


Target Point 2 - Deviation from all Participants Mean Squared Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 3.6 -8.7 25.0 0.0 -28.4 - 15.1 -30.7 - -6.7

15 -20.0 -28.5 -17.1 0.0 35.7 - -12.2 -47.1 - 42.1

30 -24.3 13.3 5.2 0.0 -13.5 - 21.1 -26.3 - -1.8

45 -13.6 7.7 23.8 0.0 -26.0 - 17.3 -28.5 - -9.2

60 -0.5 24.8 -5.4 0.0 -29.5 - 17.4 -28.8 - -6.9

75 -19.3 20.7 -13.9 0.0 0.3 - 15.7 -29.5 - -3.5

180 3.6 4.4 -7.0 0.0 -7.0 -18.3 28.0 -22.3 - -3.6

195 -4.6 6.4 -6.7 0.0 -11.7 - 26.7 -22.7 - -10.1

210 -8.0 5.8 -5.1 0.0 -12.7 - 28.6 -22.1 - -8.5

225 -18.3 20.4 -11.8 0.0 -12.0 - 25.9 -23.2 - -4.1

240 -8.5 2.4 -12.2 0.0 -5.7 - 22.8 -25.4 - 1.2

255 -24.7 -2.8 -1.0 0.0 3.5 - 16.7 -29.1 - 8.4

270 -26.4 -8.6 1.7 0.0 -7.8 -4.9 23.2 -27.4 - 22.8

285 -29.1 -6.6 -15.3 0.0 -7.7 - 24.1 -26.3 - 34.5

300 -30.4 -9.4 -5.2 0.0 -7.4 - 20.0 -28.4 - 32.3

315 -30.5 -5.1 -2.4 0.0 -11.6 - 17.9 -27.9 - 31.8

330 -33.8 -22.0 11.3 0.0 -12.6 - 23.9 -23.8 - 33.4

345 -22.3 -3.2 1.0 0.0 -15.0 - 22.2 -26.7 - 17.2

Figure 31



-30

-20

-10

0

10

20

30

-30 -20 -10 0 10 20 30

Err

or

in M

ean S

quar

ed W

ind S

pee

d

at

Tar

get

Mas

t 2 [

%]

Error in Mean Squared Wind Speed at Target Mast 1 [%]

+4.9,-17.5 ANM

ANM

-17.9,+0.6 GEO

GEO

-23.2,-6.3 LAM

LAM

0.0,0.0 MES

MES

-12.2,-10.1 MET

MET -23.8,-21.5 REP

REP

+16.4,+20.1 RTV

RTV

-31.3,-27.4 SPLSPL

-9.2,-7.3 UNJ

UNJ

-12.2,+3.3 UNK

UNK

Figure 32



0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Mea

n A

bso

lute

Err

or

in

Mea

n S

ecto

rwis

e S

quar

ed W

ind S

pee

d

at

Tar

get

Mas

t 2 [

%]

Mean Absolute Error in Mean Sectorwise Squared Wind Speed


14.4,22.1 ANM

ANM

24.8,15.8 GEO

GEO

22.0,10.1 LAM

LAM

0.0,0.0 MES

MES

21.0,18.4 MET

MET

21.7,17.3 RTVRTV

28.0,30.7 SPL

SPL

14.0,16.0 UNK

UNK

Figure 33



6.9 Evaluations Regarding Mean Cubed Wind Speed

Errors in mean cubed wind speed are in proportion to errors in the energy content of the wind. As

turbines extract only part of that, these errors are much larger than those expected for energy

production of any turbines. The results are therefore reported only for reference (Figure 34 to Figure

41).



0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n C

ubed

Win

d S

pee

d [

m3/s

3]


Target Point 1 - Mean Cubed Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 154.6 35.8 96.7 74.9 64.3 - 109.8 50.0 - 62.4

15 218.4 69.3 160.3 147.3 411.6 - 182.8 83.4 - 102.8

30 307.8 296.7 324.2 418.5 485.2 - 555.1 250.8 - 320.8

45 269.5 198.1 236.2 240.8 258.9 - 362.1 160.9 - 196.2

60 417.6 332.3 162.4 228.4 308.2 - 427.2 188.8 - 226.6

75 464.3 379.1 248.6 313.7 411.1 - 578.4 258.3 - 301.5

180 1114.0 573.1 533.7 844.9 575.3 449.8 824.3 374.8 - 470.7

195 803.1 473.1 413.0 611.0 412.4 - 683.3 315.0 - 371.7

210 800.9 511.3 396.7 679.5 443.8 - 785.6 352.1 - 434.9

225 719.0 708.2 391.8 733.8 412.8 - 909.6 403.8 - 548.6

240 465.1 321.4 167.5 380.4 216.4 - 477.6 211.9 - 331.4

255 421.1 412.7 286.7 413.0 362.6 - 550.1 245.9 - 449.1

270 350.6 344.4 241.7 386.6 330.0 298.9 530.5 232.4 - 481.3

285 417.1 350.1 302.0 504.0 481.0 - 690.4 312.9 - 680.7

300 370.8 245.6 287.7 368.5 322.1 - 582.8 266.6 - 551.6

315 333.3 220.7 296.2 403.2 303.9 - 486.9 232.5 - 437.4

330 269.1 130.2 237.3 378.3 331.3 - 476.5 230.0 - 392.9

345 195.4 79.9 133.2 174.9 177.5 - 253.1 117.3 - 181.1

Figure 34



0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

0 30 60 90 120 150 180 210 240 270 300 330 360

Mea

n C

ubed

Win

d S

pee

d [

m3/s

3]


Target Point 2 - Mean Cubed Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 103.8 73.4 114.2 106.1 53.2 - 106.8 50.0 - 84.1

15 163.5 134.7 155.6 204.8 344.7 - 178.6 83.4 - 404.2

30 237.3 502.6 406.0 557.4 322.4 - 528.9 250.8 - 404.2

45 216.3 317.0 352.5 358.8 175.1 - 341.7 160.9 - 245.5

60 316.1 462.4 287.0 396.5 189.3 - 401.4 188.8 - 294.9

75 328.1 617.7 350.8 456.3 454.5 - 546.3 258.3 - 424.1

180 615.5 609.0 484.2 542.2 495.5 406.0 795.5 374.8 - 521.0

195 444.7 526.8 410.3 391.4 388.3 - 664.8 315.0 - 400.4

210 460.3 581.9 464.8 465.5 422.9 - 752.4 352.1 - 458.7

225 428.4 828.1 493.0 527.0 500.8 - 850.0 403.8 - 578.1

240 282.0 354.0 265.8 301.5 306.4 - 453.2 211.9 - 352.8

255 257.1 408.6 389.9 368.8 437.9 - 518.9 245.9 - 487.3

270 226.9 333.2 383.2 363.8 339.3 353.0 520.2 232.4 - 539.1

285 278.8 452.8 390.6 544.6 445.7 - 690.4 312.9 - 801.6

300 250.1 383.4 400.6 476.1 396.5 - 582.8 266.6 - 696.8

315 217.9 361.6 357.4 435.4 318.8 - 486.9 232.5 - 597.6

330 183.5 257.4 389.9 421.4 284.7 - 476.5 230.0 - 556.7

345 131.7 180.7 188.9 230.1 149.0 - 253.1 117.3 - 255.4

Figure 35



-80

-60

-40

-20

0

20

40

60

80

100

120

140

160

180

200

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean C

ubed

Win

d S

pee

d [

%]


Target Point 1 - Percentage Error in Mean Cubed Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 106.3 -52.2 29.0 0.0 -14.2 - 46.5 -33.3 - -16.7

15 48.3 -53.0 8.8 0.0 179.5 - 24.1 -43.4 - -30.2

30 -26.4 -29.1 -22.5 0.0 15.9 - 32.7 -40.1 - -23.4

45 11.9 -17.7 -1.9 0.0 7.5 - 50.3 -33.2 - -18.5

60 82.8 45.5 -28.9 0.0 34.9 - 87.0 -17.3 - -0.8

75 48.0 20.9 -20.8 0.0 31.1 - 84.4 -17.6 - -3.9

180 31.8 -32.2 -36.8 0.0 -31.9 -46.8 -2.4 -55.6 - -44.3

195 31.4 -22.6 -32.4 0.0 -32.5 - 11.8 -48.4 - -39.2

210 17.9 -24.7 -41.6 0.0 -34.7 - 15.6 -48.2 - -36.0

225 -2.0 -3.5 -46.6 0.0 -43.7 - 24.0 -45.0 - -25.2

240 22.3 -15.5 -56.0 0.0 -43.1 - 25.6 -44.3 - -12.9

255 1.9 -0.1 -30.6 0.0 -12.2 - 33.2 -40.5 - 8.7

270 -9.3 -10.9 -37.5 0.0 -14.6 -22.7 37.2 -39.9 - 24.5

285 -17.2 -30.5 -40.1 0.0 -4.6 - 37.0 -37.9 - 35.1

300 0.6 -33.3 -21.9 0.0 -12.6 - 58.1 -27.7 - 49.7

315 -17.3 -45.3 -26.5 0.0 -24.6 - 20.8 -42.3 - 8.5

330 -28.9 -65.6 -37.3 0.0 -12.4 - 26.0 -39.2 - 3.9

345 11.7 -54.3 -23.9 0.0 1.5 - 44.7 -32.9 - 3.5

Figure 36



-60

-40

-20

0

20

40

60

80

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Per

centa

ge

Err

or

in M

ean C

ubed

Win

d S

pee

d [

%]


Target Point 2 - Percentage Error in Mean Cubed Wind Speed

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 -2.1 -30.8 7.6 0.0 -49.8 - 0.6 -52.9 - -20.8

15 -20.2 -34.3 -24.0 0.0 68.3 - -12.8 -59.3 - 97.3

30 -57.4 -9.8 -27.2 0.0 -42.2 - -5.1 -55.0 - -27.5

45 -39.7 -11.7 -1.7 0.0 -51.2 - -4.8 -55.1 - -31.6

60 -20.3 16.6 -27.6 0.0 -52.2 - 1.2 -52.4 - -25.6

75 -28.1 35.4 -23.1 0.0 -0.4 - 19.7 -43.4 - -7.1

180 13.5 12.3 -10.7 0.0 -8.6 -25.1 46.7 -30.9 - -3.9

195 13.6 34.6 4.8 0.0 -0.8 - 69.8 -19.5 - 2.3

210 -1.1 25.0 -0.1 0.0 -9.1 - 61.6 -24.3 - -1.5

225 -18.7 57.1 -6.4 0.0 -5.0 - 61.3 -23.4 - 9.7

240 -6.5 17.4 -11.8 0.0 1.6 - 50.3 -29.7 - 17.0

255 -30.3 10.8 5.7 0.0 18.7 - 40.7 -33.3 - 32.1

270 -37.6 -8.4 5.3 0.0 -6.7 -3.0 43.0 -36.1 - 48.2

285 -48.8 -16.9 -28.3 0.0 -18.2 - 26.8 -42.6 - 47.2

300 -47.5 -19.5 -15.9 0.0 -16.7 - 22.4 -44.0 - 46.4

315 -50.0 -17.0 -17.9 0.0 -26.8 - 11.8 -46.6 - 37.2

330 -56.5 -38.9 -7.5 0.0 -32.4 - 13.1 -45.4 - 32.1

345 -42.8 -21.5 -17.9 0.0 -35.2 - 10.0 -49.0 - 11.0

Figure 37



-80

-60

-40

-20

0

20

40

60

80

100

120

140

0 30 60 90 120 150 180 210 240 270 300 330 360

Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean C

ubed

Win

d S

pee

d [

%]


Target Point 1 - Deviation from all Participants Mean Cubed Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 77.1 -59.0 10.8 0.0 -26.3 - 25.8 -42.8 - -28.5

15 14.4 -63.7 -16.0 0.0 115.7 - -4.2 -56.3 - -46.1

30 -19.3 -22.3 -15.0 0.0 27.1 - 45.5 -34.3 - -16.0

45 6.3 -21.9 -6.8 0.0 2.1 - 42.8 -36.5 - -22.6

60 33.7 6.4 -48.0 0.0 -1.3 - 36.8 -39.6 - -27.5

75 16.9 -4.5 -37.4 0.0 3.5 - 45.6 -35.0 - -24.1

180 71.7 -11.7 -17.7 0.0 -11.3 -30.7 27.1 -42.2 - -27.4

195 52.7 -10.1 -21.5 0.0 -21.6 - 29.9 -40.1 - -29.4

210 42.4 -9.1 -29.4 0.0 -21.1 - 39.7 -37.4 - -22.6

225 16.9 15.1 -36.3 0.0 -32.9 - 47.9 -34.3 - -10.8

240 41.0 -2.6 -49.2 0.0 -34.4 - 44.8 -35.8 - 0.5

255 1.8 -0.2 -30.7 0.0 -12.4 - 33.0 -40.6 - 8.6

270 -4.8 -6.5 -34.3 0.0 -10.4 -18.8 44.1 -36.9 - 30.7

285 -14.3 -28.1 -38.0 0.0 -1.2 - 41.8 -35.7 - 39.8

300 -5.8 -37.6 -26.9 0.0 -18.1 - 48.1 -32.2 - 40.2

315 -3.8 -36.3 -14.5 0.0 -12.3 - 40.6 -32.9 - 26.3

330 -12.1 -57.5 -22.5 0.0 8.2 - 55.6 -24.9 - 28.3

345 14.9 -53.0 -21.7 0.0 4.4 - 48.9 -31.0 - 6.5

Figure 38



-80

-60

-40

-20

0

20

40

60

80

100

0 30 60 90 120 150 180 210 240 270 300 330 360

Dev

iati

on f

rom

all

Par

tici

pan

ts M

ean C

ubed

Win

d S

pee

d [

%]


Target Point 2 - Deviation from all Participants Mean Cubed Wind Speed

ANM

GEO

LAM

MET

REP

RTV

UNK


0 16.3 -17.7 27.9 0.0 -40.4 - 19.6 -44.0 - -5.8

15 -29.0 -41.5 -32.4 0.0 49.7 - -22.4 -63.8 - 75.6

30 -40.7 25.6 1.4 0.0 -19.4 - 32.1 -37.3 - 1.0

45 -21.3 15.4 28.3 0.0 -36.3 - 24.4 -41.4 - -10.6

60 -2.8 42.2 -11.7 0.0 -41.8 - 23.4 -41.9 - -9.3

75 -27.7 36.2 -22.7 0.0 0.2 - 20.4 -43.0 - -6.5

180 9.7 8.6 -13.7 0.0 -11.7 -27.6 41.8 -33.2 - -7.1

195 -5.9 11.5 -13.2 0.0 -17.8 - 40.7 -33.3 - -15.3

210 -12.1 11.2 -11.2 0.0 -19.2 - 43.7 -32.7 - -12.4

225 -30.1 35.1 -19.6 0.0 -18.3 - 38.6 -34.1 - -5.7

240 -16.0 5.5 -20.8 0.0 -8.7 - 35.0 -36.9 - 5.1

255 -38.3 -1.9 -6.4 0.0 5.1 - 24.5 -41.0 - 17.0

270 -41.1 -13.5 -0.5 0.0 -11.9 -8.3 35.1 -39.6 - 40.0

285 -45.3 -11.2 -23.4 0.0 -12.6 - 35.4 -38.6 - 57.2

300 -44.6 -15.1 -11.3 0.0 -12.2 - 29.0 -41.0 - 54.3

315 -44.1 -7.3 -8.4 0.0 -18.3 - 24.8 -40.4 - 53.2

330 -48.8 -28.1 8.9 0.0 -20.5 - 33.1 -35.8 - 55.4

345 -31.8 -6.4 -2.2 0.0 -22.8 - 31.1 -39.2 - 32.2

Figure 39



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in M

ean C

ubed

Win

d S

pee

d

at

Tar

get

Mas

t 2 [

%]

Error in Mean Cubed Wind Speed at Target Mast 1 [%]

+13.3,-23.4 ANM

ANM

-20.8,+7.2 GEO

GEO

-33.5,-10.4 LAM

LAM

0.0,0.0 MES

MES

-16.7,-12.8 MET

MET -32.6,-28.6 REP

REP

+26.9,+34.3 RTV

RTV

-42.7,-37.3 SPLSPL

-12.7,-8.9 UNJ

UNJ

-13.3,+10.6 UNK

UNK

Figure 40



0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25 30 35 40 45

Mea

n A

bso

lute

Err

or

in

Mea

n S

ecto

rwis

e C

ubed

Win

d S

pee

d

at

Tar

get

Mas

t 2 [

%]

Mean Absolute Error in Mean Sectorwise Cubed Wind Speed


28.7,29.7 ANM

ANM

30.9,23.2 GEO

GEO

30.2,13.5 LAM

LAM

0.0,0.0 MES

MES

30.6,24.7 MET

MET

36.7,27.9 RTV

RTV

38.2,41.3 SPL

SPL

21.4,27.7 UNK

UNK

Figure 41



6.10 Evaluations Regarding Wind Direction

Accurate calculation of the wind direction distribution for wind turbine positions is an important part of

any site assessment. Wind farm optimisation (or more specific: the wake effect calculation) depends to

a large part on the wind direction distribution and if the calculated distribution differs from the actually

observed one, the farm efficiency may be affected in a negative way.

For the Round Robin test, wind direction calculations are assessed in two different ways, as for the wind

speed calculations. Firstly, the results that the participants returned to DEWI based on the tab

containing all wind directions except the disturbed ones is compared to the measured ones at the two

target masts and the difference is evaluated in terms of the chi squared measure. Secondly, the results

of the participants regarding the single sector input tabs for 15 degree wind direction sectors are

evaluated, separately for each sector defined at the reference mast, regarding systematical deviations

in mean wind direction.

It must be noted that the following evaluation regarding sector-wise wind direction effects may be

inappropriate regarding the results of participant ANM, due to reasons detailed in section 6.4.

6.10.1 Qualitative Evaluation Regarding Veer Angles

If one considers the average wind direction difference between the reference mast and the two target

masts, resolved by wind direction at the reference mast, one can qualitatively investigate in how far the

participants are able to model systematic wind direction effects. Figure 60 and Figure 61 show the

measured an calculated wind direction differences (veer angles) for target mast 1 and 2 respectively.

Participant GEO does not calculate any change in wind direction at target mast 1 and 2, therefore

showing a flat calculated veer angle of zero for both masts.

Participants RTV calculates a constant veer angle of -15 deg regarding target mast 1 and 2. It is not clear

if this is intended by the participant or simply a technical issue in the calculation of the TAB (shifting by

one sector).

6.10.2 Qualitative Evaluation Regarding Veer Angles: Target Mast 1

The first characteristic feature of the sector-wise veer angles for target mast 1 versus the reference

mast is the clockwise turning of the wind direction at target mast 1 for wind from south-south-west.



The results of ANM can not by fully evaluated sector-wise, but ANM clearly shows that clockwise

turning, although he slightly overpredicts that.

A slight clockwise turning is also present in the results of LAM, but LAM heavily underpredicts the veer

angle for that direction.

MET completely fails to reproduce the clockwise turning for south-south-west wind.

Results of participant UNK show a clockwise turning quite similar to the measured one, but the

calculated peak of that veer angle is shifted clockwise by about 45 degree (this is the same „shift“ that

is also present in the speed-up factors of UNK).

The second important feature of the veer angle for target mast 1 versus the reference mast is the anti-

clockwise turning of the wind direction for wind from west and north-west.

ANM predicts that, but by far overestimates this effect. LAM predicts, on the contrary, a clockwise

turning for that direction range, while MET falis to calculate this anti-clockwise turning. Participant UNK

predicts some small anti-clockwise turning, but not for wind from west, but only for wind directions

close to north. Also the predicted turning angle is by far to small.

6.10.3 Qualitative Evaluation Regarding Veer Angles: Target Mast 2

The most characteristic feature of the veer angle regarding target mast 2 versus the reference mast is

the clockwise turning for wind from south-west, west and north-west, with the maximum veer angle

located at west. A secondary maximum of the veer angle can be found for wind from north-east.

The overall situation regarding errors in the veer angle at target mast 2 is somewhat better than at

target mast 1. From all participants, participant UNK best predicts the overall shape of the veer angle

function for the south-west, west and north-west sectors, but underestimates the effect and shows an

outliner at 15 deg. That at least one participant is able to reproduce this shape does further support the

meteorological basis of that effect and indicates that it is not a measurement error regarding the

direction. UNK also reproduces the small anti-clockwise turning for north-westerly winds.

For the north-east sector, the veer angle function of UNK shows a peak at 15 degree, i.e. the same

sector where the results of UNK showed a peak in the sector-wise speed-up factors.

Participant MET predicts some systematical change of the veer angle for south-west, west and north-

west but by far underestimates that effect. For north-west wind, MET to some degree reproduces the

small anticlockwise direction turning.



The general shape of the veer angle function calculated by LAM shows some similarity to the measured

on but for south-west, west and north-west the effect is by far underestimated.

Participant ANM predicts an overall veer angle function for target mast 2 that does only slightly differ

from that at target mast 1. It shows similarity to the measured one, with a tendency towards

overprediction, but as detailed above, sector-wise results of ANM are not fully comparable to the

results of the other participants.

6.10.4 Evaluations Regarding the Overall Wind Direction Distributions

In this section it is assessed in how far the overall measured wind direction distributions at the two

target masts can be reproduced by the participants. For that, the statistics (TABs) are considered that

the participants calculated for the two target masts on base of the wind data from the reference mast

with all wind directions except the disturbed ones. As this evaluation is not done on base of wind

direction sectors defined at the reference mast, according restrictions for sector-wise assessments

regarding participant ANM do not apply.

Figure 42 to Figure 59 show measured and calculated wind direction distributions for the two target

masts and all participants, including the „virtual“ participant SPL, for comparison. SPL represents the

reference mast data.

Figure 62 shows the results regarding the chi-squared measure which is calculated according to

( )( )∑−

=18

2

2

io

i

o

i

c

i

f

ffχ

,

where c

if is the calculated frequency and o

if is the observed frequency in the i-th undisturbed

direction sector.

Regarding the chi-squared evaluation, it turns out that differences between model performances are

much higher for target mast 2 than for target mast 1. At target mast 1 only RTV separates itself from

the other results with a significantly higher chi-squared, while there is hardly any difference in between

the other model results. Participants GEO and RTV both calculated a constant veer angle for both

masts, zero in the case of GEO and -15 in the case of RTV. While GEO well mixes among the other

results regarding chi-squared, the constant angle of -15 deg for RTV is the reason for its comparably

high chi-squared value.



Regarding the north-east wind direction range and target mast 1, all participants provide reasonable

approximations of the relative frequencies there, except RTV (for the reason see above) and ANM, who

predicts a too small share of data for that wind direction range.

Regarding the south to north-west wind direction range, the results of participant ANM differ from the

others. ANM is the only participant who approximates the relatively small share of data that falls in the

south wind direction range, which is understandable looking at the fact that ANM did provide a

reasonable (although overestimating) approximation for the veer angle function for that wind direction

range, i.e. ANM did predict a clockwise turning for wind from south-south-west.

The situation is different at target mast 2. At this mast the different models clearly perform much

different regarding their wind direction simulation performance.

That the wind direction distribution predicted by UNK fits best to the measured one at target mast 2

can be traced back to the fact that UNK did provide a reasonable approximation of the direction

dependent veer angle function, despite the unrealistic peak at 15 degree. The predicted wind direction

distribution of UNK for target mast 2 confirms the impression from the veer angle function that the

clockwise turning of the wind direction for wind from west is modelled, but underpredicted.

Although also ANM provided some approximation of the sector-wise veer angle function, the wind

direction distribution predicted by ANM for target mast 2 shows a larger deviation from the measured

one for the west sector.

Regarding the wind direction results of LAM for target mast 2 it can be observed that LAM predicts a

significant peak of the wind direction distribution function in the sector centred at 270 deg. The reason

for this peak is that, according to the veer angle function for LAM, there is clockwise turning for wind

directions smaller than 270 deg and anticlockwise turning for wind directions larger than 270 deg, while

there is no turn angle for 270 deg. Such a simulated configuration is typical for flow channelling

phenomena but it is not measured in the present case.

Surprisingly, the wind direction results of GEO mix well among the other methods regarding the chi

squared evaluation, although GEO did not predict any significant change of wind direction. This is an

indication that the other participants might partly introduce artificial flow features not present in reality

and that the assumption of a zero veer angle may produce similar or even better results than a varying

veer angle.



6.10.5 Assessment of Average Sector-Wise Wind Direction Errors

The situation regarding systematical bias in the sector-wise wind direction prediction can be

investigated on base of the results shown in Figure 67 (mean sector-wise error in wind direction).

Model results separate into three groups: A very small bias is associated with the results of ANM and

UNK while a comparably high anti-clockwise direction bias can be found in the results of RTV, LAM and

GEO. MET shows a small negative bias at target mast 2 but a high anti-clockwise direction bias at target

mast 1.

Regarding the average absolute values of sector-wise wind direction errors (Figure 68), UNK clearly

outperforms the other models at target mast 1, although at target mast 2 MET gives a comparably small

error. The constant offset of -15 deg, applied by RTV, leads to a smaller value of the absolute values of

the sector-wise errors than the application of no direction effect at all (GEO).

6.10.6 Assessment of the Directional Dispersion of the Single Sector Wind Direction Results

The participants were asked to transform to the target masts the data from the reference mast

separately for each 15 degree wind direction sector defined at the reference mast.

In the previous sections the result is assessed with respect to the mean value of the wind direction for

each of the resulting statistics (TABs). In this section, the width of the resulting wind direction

distributions is assessed, i.e. it is checked what wind direction range the resulting statistics cover.

Technically, the discrete wind direction sector probability distribution as provided by the TAB files is

considered. It is assumed that the data is equally distributed with respect to wind direction within each

15 degree wind direction sector (step-function probability). The distribution is centred at the mean

wind direction, such that clockwise turned wind directions (wrt. mean direction) cover the range 0 deg

to 180 deg and anti-clockwise turned wind directions cover the range -180 deg to 0 deg.

Starting at -180 deg, the probability function is numerically integrated until 2.5% of the data is covered.

Similar integration is performed starting at 180 deg in anti-clockwise direction. The wind direction

range, starting at the wind direction of the lower integration end point and ending at the upper

integration end point is considered as the wind direction range in that 95% of the data falls, giving

indication on the spreading of the wind direction distribution function. Clearly, this approach is based

on the assumption that the distribution is unimodal and falls off quickly. This is not the case for

participant ANM.



Figure 65 and Figure 66 report that range for the measured data and the participating models.

For both masts, the model results can clearly be separated into two groups: All models, except ANM

show small direction spreadings below 30 degree where GEO and RTV show a spreading equal to that of

the measured sector-wise data at the reference mast, reflecting the fact that GEO did not apply any

direction change and RTV did apply a constant change of -15 degree.

That the spreading of ANM is significantly higher than that of the other participants can be traced to

their different treatment of the site-to-site relationships for the sector-wise evaluation in the test (see

section 6.4).

In can be observed that the measured spreading is significantly higher than that predicted by the

participants (except for ANM who predicts a higher spreading). The measured dispersion of the sector-

wise wind direction varies in between 60 deg and 90 deg for target mast 1 and between 55 deg and

208 deg for target mast 2, which is significantly higher than predicted by all participants except ANM. It

should however be noted that the evaluations could only be performed using the whole wind speed

range, although wind vanes tend to show meandering behaviour for low wind speeds, which should be

the main reason for the higher measured spreading. On the other hand, for models that base on the

determination of direction dependent site-to-site transfer functions, the introduction of some wind

direction fluctuation may possibly be worth to consider. In the case of turbine wake modelling the

meandering of the wakes (wind direction fluctuations) at least plays some role.



5

10

5 10

ANM - Target Point 1Chi Squared=5.43E-006

E

S

W

N

ANM Measured

Figure 42

5

10

5 10

ANM - Target Point 2Chi Squared=1.54E-005

E

S

W

N

ANM Measured

Figure 43

5

10

5 10

GEO - Target Point 1Chi Squared=4.98E-006

E

S

W

N

GEO Measured

Figure 44

5

10

5 10

GEO - Target Point 2Chi Squared=7.90E-006

E

S

W

N

GEO Measured

Figure 45

5

10

5 10

LAM - Target Point 1Chi Squared=6.64E-006

E

S

W

N

LAM Measured

Figure 46

5

10

5 10

LAM - Target Point 2Chi Squared=1.49E-005

E

S

W

N

LAM Measured

Figure 47



5

10

5 10

MET - Target Point 1Chi Squared=6.07E-006

E

S

W

N

MET Measured

Figure 48

5

10

5 10

MET - Target Point 2Chi Squared=7.90E-006

E

S

W

N

MET Measured

Figure 49

5

10

5 10

REP - Target Point 1Chi Squared=6.15E-006

E

S

W

N

REP Measured

Figure 50

5

10

5 10

REP - Target Point 2Chi Squared=8.08E-006

E

S

W

N

REP Measured

Figure 51

5

10

5 10

RTV - Target Point 1Chi Squared=1.10E-005

E

S

W

N

RTV Measured

Figure 52

5

10

5 10

RTV - Target Point 2Chi Squared=1.91E-005

E

S

W

N

RTV Measured

Figure 53



5

10

5 10

SPL - Target Point 1Chi Squared=4.95E-006

E

S

W

N

SPL Measured

Figure 54

5

10

5 10

SPL - Target Point 2Chi Squared=7.83E-006

E

S

W

N

SPL Measured

Figure 55

5

10

5 10

UNJ - Target Point 1Chi Squared=6.70E-006

E

S

W

N

UNJ Measured

Figure 56

5

10

5 10

UNJ - Target Point 2Chi Squared=1.32E-005

E

S

W

N

UNJ Measured

Figure 57

5

10

5 10

UNK - Target Point 1Chi Squared=5.07E-006

E

S

W

N

UNK Measured

Figure 58

5

10

5 10

UNK - Target Point 2Chi Squared=3.93E-006

E

S

W

N

UNK Measured

Figure 59



-40

-30

-20

-10

0

10

20

30

40

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

1)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 1Participant ANM

ANMMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360d

ir (

Tar

get

Mas

t 1

) -

dir

(R

efer

ence

Mas

t) [


Veer Angle at Target Mast 1Participant GEO

GEOMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

1)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 1Participant LAM

LAMMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arget

Mas

t 1)

- dir

(R

efer

ence

Mas

t) [

-]


Veer Angle at Target Mast 1Participant MET

METMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

1)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 1Participant RTV

RTVMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

1)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 1Participant UNK

UNKMeasured

Figure 60: Veer Angles for Target Mast 1 versus Reference Mast.



-40

-30

-20

-10

0

10

20

30

40

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arget

Mas

t 2)

- dir

(R

efer

ence

Mas

t) [

-]


Veer Angle at Target Mast 2Participant ANM

ANMMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360dir

(T

arget

Mas

t 2)

- dir

(R

efer

ence

Mas

t) [


Veer Angle at Target Mast 2Participant GEO

GEOMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

2)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 2Participant LAM

LAMMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

2)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 2Participant MET

METMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

2)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 2Participant RTV

RTVMeasured

-20

-10

0

10

20

0 30 60 90 120 150 180 210 240 270 300 330 360

dir

(T

arg

et M

ast

2)

- d

ir (

Ref

eren

ce M

ast)

[-]


Veer Angle at Target Mast 2Participant UNK

UNKMeasured

Figure 61: Veer Angles for Target Mast 2 versus Reference Mast.



2

4

6

8

10

12

14

16

18

20

2 4 6 8 10 12 14 16 18 20

Chi

Squar

ed

at

Tar

get

Mas

t 2 *

1E

6 [

-]

Chi Squared at Target Mast 1 * 1E6 [-]

5.43,15.37 ANM

4.98,7.90 GEO

6.64,14.89 LAM

6.07,7.90 MET

6.15,8.08 REP

11.03,19.10 RTV

4.95,7.83 SPL

6.70,13.18 UNJ

5.07,3.93 UNK

Figure 62



-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

40

0 30 60 90 120 150 180 210 240 270 300 330 360

Err

or

in M

ean W

ind D

irec

tion [

deg

]


Target Point 1 - Error in Mean Wind Direction

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 -21.9 1.8 6.8 0.0 1.8 - -13.2 1.8 - 1.0

15 -9.2 -0.7 0.9 0.0 -1.4 - -15.7 -0.7 - -0.2

30 -7.5 -0.8 1.2 0.0 -2.7 - -15.8 -0.8 - 0.4

45 8.6 -0.3 1.5 0.0 -3.0 - -15.3 -0.3 - 1.8

60 35.2 0.3 1.2 0.0 -0.7 - -14.7 0.3 - 3.5

75 25.7 5.8 6.5 0.0 5.8 - -9.2 5.8 - 9.0

180 9.0 -5.6 -3.4 0.0 -6.5 -5.8 -20.6 -5.6 - -6.5

195 5.1 -9.6 -8.6 0.0 -9.6 - -24.6 -9.6 - -8.3

210 7.8 -8.3 -7.1 0.0 -8.3 - -23.3 -8.3 - -4.7

225 6.1 -3.6 -3.1 0.0 -3.6 - -18.6 -3.6 - 1.2

240 5.4 -1.0 2.4 0.0 0.3 - -16.0 -1.0 - 4.9

255 9.0 3.1 7.3 0.0 4.3 - -11.9 3.1 - 9.1

270 7.6 4.2 5.0 0.0 4.2 4.4 -10.8 4.2 - 8.6

285 1.5 3.6 7.3 0.0 4.6 - -11.4 3.6 - 6.2

300 -6.8 4.9 8.2 0.0 5.7 - -10.1 4.9 - 5.5

315 -13.5 4.2 7.5 0.0 4.2 - -10.8 4.2 - 4.2

330 -17.7 4.2 10.1 0.0 5.2 - -10.8 4.2 - 3.0

345 -17.4 5.1 11.7 0.0 5.1 - -9.9 5.1 - 4.0

Figure 63



-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

40

0 30 60 90 120 150 180 210 240 270 300 330 360

Err

or

in M

ean W

ind D

irec

tion [

deg

]


Target Point 2 - Error in Mean Wind Direction

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 -14.3 4.5 4.7 0.0 3.7 - -10.5 4.5 - 2.0

15 -8.6 -4.1 -3.4 0.0 -5.7 - -19.1 -4.1 - 10.2

30 -8.2 -4.9 -2.9 0.0 -5.8 - -19.9 -4.9 - -5.6

45 -0.2 -4.6 -1.3 0.0 -6.0 - -19.6 -4.6 - -2.5

60 21.6 -4.5 -1.1 0.0 -2.3 - -19.5 -4.5 - -0.3

75 16.5 -1.7 2.2 0.0 0.4 - -16.7 -1.7 - 3.0

180 14.2 -2.3 -0.2 0.0 -4.1 -3.2 -17.3 -2.3 - -4.9

195 11.2 -6.7 -5.4 0.0 -7.8 - -21.7 -6.7 - -7.5

210 14.3 -9.6 -8.5 0.0 -8.7 - -24.6 -9.6 - -6.3

225 5.8 -9.4 -7.7 0.0 -8.3 - -24.4 -9.4 - -4.5

240 2.3 -11.5 -6.5 0.0 -9.3 - -26.5 -11.5 - -5.0

255 1.1 -10.9 -6.7 0.0 -8.8 - -25.9 -10.9 - -3.6

270 -3.3 -12.2 -12.2 0.0 -11.4 -12.8 -27.2 -12.2 - -5.8

285 -9.0 -10.2 -13.4 0.0 -9.2 - -25.2 -10.2 - -6.0

300 -11.8 -5.7 -9.5 0.0 -5.7 - -20.7 -5.7 - -4.4

315 -12.0 -0.2 -5.5 0.0 -1.3 - -15.2 -0.2 - -0.2

330 -15.9 1.2 0.0 0.0 -2.0 - -13.8 1.2 - 0.2

345 -14.4 3.7 2.2 0.0 2.6 - -11.3 3.7 - 0.7

Figure 64



0

30

60

90

120

150

180

210

240

270

300

330

360

0 30 60 90 120 150 180 210 240 270 300 330 360Win

d D

irec

tion R

ange

wit

h 9

5%

of

Dat

a In

side

[deg

]


Target Point 1 - Wind Direction Range with 95% of Data Inside

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 279.2 14.2 28.3 84.0 14.2 - 14.4 14.2 - 22.7

15 288.7 14.2 26.0 70.3 21.4 - 14.2 14.2 - 19.2

30 302.2 14.2 27.4 62.1 26.6 - 14.2 14.2 - 24.7

45 342.3 14.2 26.4 78.1 27.5 - 14.2 14.2 - 27.1

60 301.2 14.2 23.2 81.8 23.9 - 14.2 14.2 - 27.8

75 283.7 14.2 22.0 93.5 14.2 - 14.2 14.2 - 27.8

180 236.9 14.3 27.0 74.0 23.1 14.5 14.3 14.3 - 23.1

195 254.4 14.3 23.9 79.1 14.3 - 14.3 14.3 - 25.1

210 237.1 14.3 25.0 71.9 14.3 - 14.3 14.3 - 28.0

225 255.4 14.3 18.8 68.7 14.3 - 14.3 14.3 - 28.3

240 247.9 14.3 27.9 58.6 25.2 - 14.3 14.3 - 28.4

255 245.3 14.3 28.1 60.3 24.7 - 14.3 14.3 - 28.4

270 215.1 14.3 22.2 63.3 14.3 14.5 14.3 14.3 - 28.2

285 191.0 14.3 28.1 61.4 23.7 - 14.3 14.3 - 27.4

300 180.2 14.4 27.8 68.0 23.0 - 14.3 14.4 - 19.7

315 211.4 14.4 27.9 65.3 14.4 - 14.4 14.4 - 14.4

330 234.5 14.4 28.4 70.0 23.8 - 14.4 14.4 - 24.7

345 246.1 14.4 28.5 79.7 14.4 - 14.4 14.4 - 24.6

Figure 65



0

30

60

90

120

150

180

210

240

270

300

330

360

0 30 60 90 120 150 180 210 240 270 300 330 360Win

d D

irec

tion R

ange

wit

h 9

5%

of

Dat

a In

side

[deg

]


Target Point 2 - Wind Direction Range with 95% of Data Inside

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNK


0 270.7 14.2 21.0 208.2 22.7 - 14.4 14.2 - 27.3

15 317.0 14.2 31.7 139.3 26.0 - 14.2 14.2 - 29.0

30 294.2 14.2 38.0 104.1 23.6 - 14.2 14.2 - 29.0

45 319.4 14.2 27.9 104.2 25.6 - 14.2 14.2 - 27.4

60 283.5 14.2 27.9 113.2 27.0 - 14.2 14.2 - 28.1

75 278.9 14.2 28.0 126.6 26.9 - 14.2 14.2 - 28.3

180 274.3 14.3 26.9 70.2 26.4 23.1 14.3 14.3 - 27.4

195 284.6 14.3 25.4 91.2 24.5 - 14.3 14.3 - 32.8

210 222.7 14.3 24.4 83.9 23.6 - 14.3 14.3 - 27.9

225 241.7 14.3 26.2 85.2 24.4 - 14.3 14.3 - 28.3

240 226.5 14.3 28.3 88.8 27.0 - 14.3 14.3 - 28.5

255 232.0 14.3 28.1 85.4 26.9 - 14.3 14.3 - 28.5

270 209.5 14.3 14.3 64.4 22.7 19.8 14.3 14.3 - 28.5

285 187.1 14.3 27.8 55.1 23.7 - 14.3 14.3 - 28.1

300 174.1 14.4 28.1 66.9 14.4 - 14.3 14.4 - 25.2

315 205.2 14.4 30.7 71.9 24.4 - 14.4 14.4 - 14.4

330 206.9 14.4 25.7 86.1 27.8 - 14.4 14.4 - 23.9

345 237.1 14.4 37.4 156.7 24.2 - 14.4 14.4 - 27.7

Figure 66



-30

-25

-20

-15

-10

-5

0

5

10

-30 -25 -20 -15 -10 -5 0 5 10

Mea

n S

ecto

rwis

e E

rror

in

W

ind D

irec

tion

at

Tar

get

Mas

t 2 [

deg

]

Mean Sectorwise Error in Wind Direction

at Target Mast 1 [deg]

1.5,-0.6 ANM

-19.6,-24.9 GEO

-16.9,-24.2 LAM

0.0,0.0 MES

-19.7,-5.0 MET

-14.6,-19.9 RTV

-19.6,-24.9 SPL

2.4,-2.3 UNK

Figure 67



0

5

10

15

20

25

30

0 5 10 15 20 25 30

Mea

n A

bso

lute

Sec

torw

ise

Err

or

in

W

ind D

irec

tion

at

Tar

get

Mas

t 2 [

deg

]

Mean Absolute Sectorwise Error in Wind Direction

at Target Mast 1 [deg]

11.9,10.3 ANM

23.5,25.5 GEO

24.8,24.7 LAM

0.0,0.0 MES

24.1,5.7 MET

14.6,19.9 RTV

23.5,25.5 SPL

4.6,4.0 UNK

Figure 68



6.11 Single Turbine AEP

DEWI repeated the evaluation regarding mean wind speed with an energy yield instead of mean wind

speed. This energy yield is calculated separately with the power curves in Table 7.

The selection of these power curves is by chance. The list of turbines is not ordered according to any

particular rule. But the aim is to give an impression of the error of the models in predicting the AEP of a

contemporary, middle-class wind turbine. All these turbines and other turbines may or may not be

suitable for the site. DEWI calculated the energy yield according to the procedure specified in the latest

IEC standard on power curve measurement. The cut-out wind speed is recalculated according to

information available on the cut-out behaviour. Although necessary for energy yield assessment, no

density correction and no anemometer type correction is applied because we consider this as

meaningless for the Round Robin Test evaluations. The hub height is set equal to the height of the

anemometer at the target mast (80 m).

Siemens/AN Bonus 2.3MW MKII/Siemens 2.3MW/93-VS 80 measured Risoe-I-2444-1 (FGW Technische Richtlinien Teil 2 rev 14).

Enercon E-70 E4 measured DEWI PV 0308-08.2

GE Wind Energy 1.5 s (was Tacke) measured DEWI PV 0001-01

NEG NM82/1500 kW measured Windtest Grevenbroich LK 01 001 B1

NORDEX S-77/1500 measured Windtest Grevenbroich LK02001B1A6

Vestas V80-2.0MW 105.1 dB measured Windtest KWK WT1813/01

Table 7: Power curves used for the calculation of the single turbine AEP and the Farm Energy Yield.



6.12 Energy Yield Results: Single Turbine AEP

In this section we present results concerning the capabilities of the different models of predicting the

annual energy production (AEP) of a single wind turbine. The wind turbine is ideally placed in the same

position as one of the two target masts with a hub height of 80 m. The same evaluation was repeated

for six different wind turbines and for both target masts. Information on the power curves of the wind

energy converters chosen for the test and details on the calculation procedure are reported in Section

6.11. The results of the evaluation are shown in tabular form in Table 8. It has been found that values of

relative error are approximately comprised in the range [0%-30%]. Due to the features of the selected

power curves and to properties of the of wind conditions at the site, wind turbines are generally

associated to different relative error. In other words, for the wind distribution at the site, for some wind

turbines the AEP might be „harder to predict” than for others. For this reason the relative error of one

model in predicting the energy yield of one turbine can only be compared with errors of other models

within the same turbine type. There was no clear evidence that AEP errors at target mast 1 were

significantly different from AEP errors at mast 2. As can be noticed in Figure 69 to Figure 74, for a

number of participants (namely REP, UNJ, MET, RTV ) the relative error is similar for both masts.

Despite a large spread in the results, all models were able to improve the initial information of the

reference mast and to obtain a lower relative error than SPL which was in the range [-27%, - 36%].



AN9323 EN7120 GE7015 NM8215 NOS7715 VE8020

ANM 5.62 6.59 6.49 1.21 3.96 3.65

GEO -20.28 -19.52 -20.54 -19.81 -20.55 -19.92

LAM -24.89 -26.40 -26.77 -21.06 -24.29 -24.91

MES 0.00 0.00 0.00 0.00 0.00 0.00

MET -12.25 -12.80 -12.98 -10.59 -11.99 -12.15

REP -24.97 -26.12 -26.44 -21.83 -24.50 -24.91

RTV 16.50 18.18 17.78 11.65 15.18 15.17

SPL -34.15 -35.63 -36.12 -30.08 -33.57 -34.16

UNJ -8.73 -9.12 -9.25 -7.55 -8.62 -8.70

UNK -12.84 -12.73 -13.04 -12.90 -13.29 -13.31

ANM -16.22 -17.71 -17.27 -13.86 -15.75 -16.44

GEO -3.31 -1.41 -2.61 -6.28 -4.13 -3.65

LAM -6.28 -7.22 -7.14 -4.52 -5.95 -6.47

MES 0.00 0.00 0.00 0.00 0.00 0.00

MET -10.39 -10.92 -10.93 -9.35 -10.24 -10.60

REP -23.78 -24.82 -25.06 -21.32 -23.41 -23.98

RTV 20.32 22.25 22.06 14.74 18.84 18.86

SPL -30.55 -31.87 -32.30 -27.14 -30.09 -30.72

UNJ -7.34 -7.59 -7.67 -6.80 -7.37 -7.50

UNK 3.09 4.23 4.09 0.19 2.03 2.23

Target Mast 1

Target Mast 2

Table 8: Relative error for a single turbine AEP in table form. All values in percent.



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]

Error in AEP at Target Mast 1 [%]

Relative AEP Error WTG AN9323

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 69: Relative error in predicting the annual energy yield of a single AN Bonus 2.3MW turbine, ideally placed at

the position of mast 1 (x axis) and mast 2 (y axis).



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]


Relative AEP Error WTG EN7120

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 70: Relative error in predicting the annual energy yield of a single Enercon E70 E4, ideally placed at the

position of mast 1 (x axis) and mast 2 (y axis).



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]


Relative AEP Error WTG GE7015

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 71: Relative error in predicting the annual energy yield of a single GE Wind Energy 1.5 s, ideally placed at the




-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]


Relative AEP Error WTG NM8215

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 72: Relative error in predicting the annual energy yield of a single NEG NM82/1500, ideally placed at the




-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]


Relative AEP Error WTG NOS7715

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 73: Relative error in predicting the annual energy yield of a single NORDEX S-77/1500, ideally placed at the




-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in A

EP

at

Tar

get

Mas

t 2 [

%]


Relative AEP Error WTG VE8020

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 74: Relative error in predicting the annual energy yield of a single Vestas V80-2.0MW, ideally placed at the




6.13 Wind Farm Energy Yield Results

The same evaluation exercise as presented in Section 6.12 for a single wind turbine was repeated for a

wind farm. For each of the two target masts, DEWI created, from the predicted wind data tab, a wind

resource grid. This resource grid has been used for the entire wind farm area. The wind resource grid

specifies the wind resource at each point in the form of overall and sector-wise Weibull A and k

parameters and wind direction frequencies. The grid contains the same wind resource at all points.

Using that resource grid, DEWI did calculate the energy yield for a wind farm of 16 turbines, arranged

on a square grid with a row and column spacing of 500 m. The rows are aligned towards geographic

north. For the wake calculations the „Jensen” Model is used with standard parameters (wake decay

0.075). It must be noted that this is a wind farm layout that is just selected to simplify the evaluation. It

is not fitted to the site in any way.

The wind farm is equipped with each of the turbines specified in Table 7 where the thrust curves are

used as specified by the manufacturer. One turbine type is used for all turbines in the farm, resulting in

6 different farms in total. The resulting farm energy yields, obtained with the participants results, will

be compared to farm energy yields obtained with the according measured data. Results are presented

as relative deviation in Table 9 and in Figure 76 - Figure 80.

Most of the considerations reported for the single turbine AEP can be applied to wind farm energy

yield. The relative errors are comprised in the range [0%- 30%]. Most of the models present similar

error for the two masts. As for the single turbine energy yield, all models were able to improve the

initial information and perform better than the virtual participant „SPL”.



Table 9: Relative error for a wind Farm Energy Yield in table form. All values in percent.

AN9323 EN7120 GE7015 NM8215 NOS7715 VE8020

ANM 2,39 3,54 2,79 -0,11 1,43 2,53

GEO -24,93 -24,35 -25,42 -24,89 -25,35 -24,95

LAM -28,11 -28,87 -29,44 -24,38 -27,13 -28,09

MES 0,00 0,00 0,00 0,00 0,00 0,00

MET -14,91 -15,06 -15,36 -13,33 -14,48 -14,85

REP -29,11 -29,66 -30,29 -25,97 -28,36 -29,11

RTV 16,16 17,65 17,24 11,92 14,72 16,21

SPL -36,73 -37,53 -38,43 -32,71 -35,85 -36,78

UNJ -11,51 -11,72 -11,98 -10,33 -11,25 -11,52

UNK -15,83 -15,53 -16,16 -15,66 -16,03 -15,84

ANM -21,00 -21,37 -21,83 -18,36 -20,26 -20,92

GEO -5,98 -4,49 -5,67 -8,94 -7,37 -6,02

LAM -9,36 -9,56 -9,61 -7,45 -8,61 -9,20

MES 0,00 0,00 0,00 0,00 0,00 0,00

MET -12,70 -12,60 -12,87 -11,55 -12,32 -12,56

REP -27,17 -27,43 -28,08 -24,57 -26,52 -27,09

RTV 20,47 22,41 22,10 15,37 18,90 20,66

SPL -32,95 -33,52 -34,43 -29,43 -32,17 -32,94

UNJ -9,86 -9,85 -10,17 -9,22 -9,75 -9,83

UNK 1,54 2,76 2,23 -1,04 0,62 1,65

Target Mast 1

Target Mast 2



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]

Error in FEY at Target Mast 1 [%]

Relative FEY Error WTG AN9323

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 75: Relative error in predicting the annual energy yield of a wind farm (Siemens/AN Bonus 2.3MW MKII

WTGs).



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]


Relative FEY Error WTG EN7120

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 76: Relative error in predicting a farm energy yield (Enercon E-70 E4 WTGs).



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]


Relative FEY Error WTG GE7015

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 77: Relative error in predicting a farm energy yield (GE Wind Energy 1.5s WTGs)



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]


Relative FEY Error WTG NM8215

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 78: Relative error in predicting a farm energy yield (NEG NM82 WTGs)



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]


Relative FEY Error WTG NOS7715

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 79: Relative error in predicting a farm energy yield (NORDEX S-77/1500 WTGs)



-40

-30

-20

-10

0

10

20

30

40

-40 -30 -20 -10 0 10 20 30 40

Err

or

in F

EY

at

Tar

get

Mas

t 2 [

%]


Relative FEY Error WTG VE8020

ANM

GEO

LAM

MES

MET

REP

RTV

SPL

UNJ

UNK

Figure 80: Relative error in predicting a farm energy yield (Vestas V80-2.0MW WTGs)



7 Conclusions

The results of 8 flow models operated by the participants ANM, GEO, LAM, MET, REP, RTV, UNJ and

UNK were analysed and compared to measured data regarding one site equipped with three met

masts. The results are therefore valid only for this particular site.

The results of UNJ and REP are incomplete and cannot be fully compared to the others. The results of

ANM can not in all aspects be fully compared to the other models because the modelling and/or data

processing approach of ANM differs from the others for the sector-wise data.

The site under consideration in the test is a plateau located on the island Sardinia in the Mediterranean

Sea. It turns out that the wind conditions on site are by far more complex than suggested by the height

structure. Accordingly, the model results differ from each other considerably in almost all aspects.

To some part, the difference in mean wind speed between the reference mast and the two target masts

is constituted by the measurement height difference of 37 m that has to be bridged by the models.

There is a tendency of all participant's results, except RTV, to underestimate mean wind speed. which is

a critical issue in the context of the assessment of the suitable wind turbine class for a site.

Participant RTV uses a different type of model and his results differ significantly from the others, mainly

because he calculates much less directional variation of the relationship between the test wind

measurements and because it is the only model showing a significant overestimation (and not an

underestimation) of wind speed and energy yield at this site. This is a statement regarding the

inhomogeneity of the modelling approaches and not a statement regarding the accuracy of the results

of RTV.

None of the participants is able to model the characteristic flow features for the site in all details for

both target masts but the results suggest that in some cases specific meteorological effects are

reproduced for either mast 1 or mast 2 but not for both masts at the same time by the same

participant.

The analysis of the results in terms of 15 degree wind direction sectors shows insufficient results for all

participants except for LAM (only at target mast 2), ANM (only at target mast 1) and partly for UNK. The

observed large sector-wise errors partly average out in the calculation of the energy yield.

The test was set up carefully, especially regarding the meteorological and geographical data, which is

comparable to or of better quality than what is usually used for wind and site assessment in central



Europe. That the site-specific effects can be reproduced by some models (although none of the models

reproduces all effects), substantiates that these effects are real meteorological effects and not

measurement errors. Furthermore, it indicates that the geographical and meteorological data used in

the test is sufficient for the calculation of those effects.

It must be concluded that the models partly describe flow features that are not present in reality, and,

for this site, the dominant flow effects are, with exceptions, not sufficiently modelled.

Two of the model results showed for a specific wind direction sector strong artefacts that may be either

modelling problems or problems with adaptation to the test procedure. Such artefacts should be

excluded by the participants through specific quality control methods regarding the simulation results.

Altogether, we consider the round robin test procedure established in this research as suitable to the

problem area and useful for the purpose of model testing. It may be used as a template for further,

similar tests, that should then also contain a strict criterion that states whether a model „passed“ the

test or „did not pass“ the test. Such a criterion must not only refer to the relative deviation of the

participants results from a mean value of all participants results, due to the inhomogeneity of the

models and the presence of outliners.

If, instead, the criterion is based on the deviation from measured data, there are difficult problems to

overcome: firstly, for the measurements there are very high quality demands that should be fulfilled by

wind measurements specifically adapted and operated for this purpose. These costs must not be

considered as prohibitive. Secondly, there is no easy way to define the maximum deviation from the

measured data that allows to assign a „passed“ to a model. Thirdly, there would be high nondisclosure

demands on the measured data of the target masts.

Only if further developments improve the homogeneity of the models, the deviation from the mean of

all participants can be used as a test criterion. Such a test criterion is required.

To resolve the problem, we propose to focus on improvements of the homogeneity and transparency of

the models.

In particular, model validation becomes meaningless if changes of model software (either the core or

the pre- and postprocessing) take place behind the scenes. Validation results are tied to a model in a

specific state (version). However, it is not clear what version control system the participants use.

The flow modelling is tied to the company offering the service. For both the users of commercial

software and for the users of in-house software, a harmonisation of the wind modelling work is



required. The first important step towards such a harmonisation is the present round robin test. The

next step could be the creation of board similar to MEASNET, like “SIMULNET”, which maintains

harmonisation of the services provided by the members and constantly follows the state of the art in

this field. One could benefit from the experience gained in the creation of MEASNET. In particular, one

would have to find ways to harmonise the flow modelling services and to conduct quality checks,

possibly using this round robin as a template.

From the experience acquired during this Round Robin we judge as necessary an improvement of the

documentation of the flow modelling work, especially regarding spatial resolution, directional and wind

speed resolution, domain size, convergence behaviour and exact specification and version of the model

used.

Although this is not foreseen at the moment, the further development may greatly benefit if one would

agree on one single model, which may incorporate a mesoscale model from the family of MM5/WRF

and a microscale model from the family of steady state Navier-Stokes solvers with a two equation

turbulence closure. An optimum transparency could be achieved with an open source model that is

maintained by the community along with strict application rules.

As this is not expected, significant improvements could also be achieved if one agrees on only one pre-

and postprocessing software, while keeping the flow model cores closed. In our opinion, the difference

between different microscale CFD models regarding their results may originate partly from differences

in the pre- and postprocessing and not only from differences in the core solvers.

Although the results of this round robin show that there is still a strong need of research and

development necessary in the field of CFD modelling, it is assumed that a carefully designed and

applied flow model will provide better accuracy than WASP, because there are less simplifying

assumptions regarding the processes modelled and important, relevant processes, especially in

complex terrain, are not modelled at all by WASP. Flow modelling is today a standard engineering tool

that is successfully used in many industrial sectors and one must take advantage of the capabilities for

wind energy applications. An important design driver of the WASP software were the very limited

capabilities of the computer hardware at that time. Today, there is no justification at all for the many

simplifications that are incorporated into WASP and there is no reason why one should trust more the

results of WASP than the results of an appropriate flow model. However, WASP was designed

specifically for wind energy applications, while flow models for wind energy applications are in general



adapted versions of flow models for other purposes (general CFD, large-scale meteorological

modelling). A flow model may very accurately model complex terrain wind flow, but problems may be

related to the pre- and postprocessing, e.g. the wind data or the technical properties of the wind

turbines may not be handled appropriately. It is therefore important not to focus only on the core flow

model but also on the pre- and postprocessing.

Although WASP is a standardised computer code, the results obtained with it depend on the input data

and the way it is handled (roughness map, height map, wind data, possibly heat flux adaptations,...).

This dependency is present in a WASP calculation as it is in a calculation with a flow model, although in

the case of a flow model, the model operator has flexibility regarding the spatial discretisation (grid

design). It is required to combine the higher accuracy of the flow models with the required certainty

regarding homogeneity of the results by taking next steps in the harmonisation process.



8 Appendix

8.1 Overview of the properties of the on-site measurements

In this section details of the on-site wind data of the three met masts are shown. The data is

represented by the common time series (common time steps) of the following sensors:

1. Reference Mast, Anemometer at 43m height agl.(Thies First Class).

2. Reference Mast, Wind Vane at 42m height agl.

3. Target Mast 1, Anemometer at 80m height agl.(Thies First Class).

4. Target Mast 1, Wind Vane at 78m height agl.

5. Target Mast 2, Anemometer at 80m height agl. (Thies First Class).

6. Target Mast 2, Wind Vane at 78m height agl.

It has been verifies that the data logger internal clocks run synchronously. Additionally, this common

time series was filtered with respect to the wind direction at target mast 1 (42m height) such that data

from within the sector [82.5 °,172.5 °] is excluded in order to minimise influences of the mast top on

the anemometer. The resulting time series ranges from 2005-11-09 18:10:00 until 2006-07-01 00:00:00

(233 days). The total number of time steps amounts to 27254 which would correspond to a continuous,

complete time series of 189 days (18.9% missing data , mainly due to directional filtering).

Within the test, sector-wise analyses are carried out (15 degree wind direction sectors). It has been

verified that each sector contains enough data sets in order to yield representative results. The number

of data in each sector is reported in Table 11.

Table 10 summarises the mean wind speeds measured at the three masts for the wind speed sensors

used in the test. The Weibull statistics and wind direction distributions are shown in Figure 81, Figure

82 and Figure 83.



Wind Speed Sensor Mean Wind Speed

(Calculated from the

Time Series) [m/s]

Ratio of Mean Wind Speed to

Mean Wind Speed Measured at

Reference Mast [-]

Reference Mast at 43m 5.07 100.0%

Target Mast 1 at 80m 6.10 120.4%

Target Mast 2 at 80m 5.95 117.5%

Table 10: Mean Wind Speeds for the site masts.

There is significant increase in mean wind speed from the reference mast to each of the two target

masts. Mean wind speed is slightly higher at target mast 1 than at target mast 2.

Regarding the overall k-factors, there is hardly any difference between the two target masts. The k-

factor is almost the same for target mast 1 and 2 (1.85/1.84). At the reference mast, the k-factor is

slightly lower (1.81).



N

W E

S

Figure 81: Wind direction and wind speed distribution at the reference mast at 43m agl.

(The mean wind U in the graphs is calculated from the wind speed distribution, which has been fitted to the binned

wind data and therefore it is usually not equivalent to the wind data itself.)

N

W E

S

Figure 82: Wind direction and wind speed distribution at the target mast 1 at 80m agl.





N

W E

S

Figure 83: Wind direction and wind speed distribution at the target mast 2 at 80m agl.





8.1.1 Wind Direction Effects

There are significant differences in the wind direction distributions between the three masts for wind

from south, south-west, west and north-west. For wind from north-east, the wind direction

distributions at the three masts do not differ very much. A comparison of the wind direction

distributions of the reference mast and target mast 1 shows that there is significantly more often wind

from south-west at target mast 2 than at the reference mast. Also, the share of data with a wind

direction around south decreases at target mast 1, compared to the reference mast. This fact is

associated with the fact that, on average, the wind from south is turned by about 5 degree to 10 degree

clockwise at target mast 1, compared to the reference mast. At the same time, for the wind directions

west to north-west, wind direction is turned anticlockwise by about 5 degree at target mast 1,

compared to the reference mast. This direction dependent effect is shown also in Figure 84,where the

sector-wise averaged wind direction difference between the two masts is shown. The situation

regarding systematic wind direction effects at target mast 2 is different. Wind, that blows, at the

reference mast, from east, is turned clockwise towards north-north-west at target mast 2. This effect is

also clearly visible in the wind direction difference plot (Figure 85). For wind from west and south-west

(at the reference mast), wind direction is turned clockwise by bout 10 degree. This turning vanishes for

wind from north-north-west, leading to slight peak in the wind direction distribution at target mast 2 at

north-west-west.

For the evaluations regarding wind direction differences, all data with wind speeds below 1m/s were

excluded.



-60

-40

-20

0

20

40

60

0 45 90 135 180 225 270 315 360

Win

d D

irec

tion D

iffe

rence

Dir

(Tar

get

Mas

t1)

- D

ir(R

efer

ence

Mas

t) [

deg

]

Wind Direction Sector Centre (Reference Mast) [deg]

Wind Direction DifferencesTarget Mast 1 vs. Reference Mast

Figure 84: Wind direction differences between target mast 1 and reference mast.



-60

-40

-20

0

20

40

60

0 45 90 135 180 225 270 315 360

Win

d D

irec

tion D

iffe

rence

Dir

(Tar

get

Mas

t2)

- D

ir(R

efer

ence

Mas

t) [

deg

]


Wind Direction DifferencesTarget Mast 2 vs. Reference Mast

Figure 85: Wind direction difference between target mast 2 and reference mast.



8.1.2 Speed-up Factors

The common time series of the wind data at the three masts is subdivided into wind direction sectors

with respect to the wind direction at the reference mast (15 deg sectors). For each sector, the ratio of

the mean wind speeds at one of the target masts to the mean wind speed at the reference mast is

evaluated and reported in Table 11 and Figure 86.

There are large differences between the speed-up factors for target mast 1 and target mast 2. For wind

from north-east, the speed-up factors are significantly higher for target mast 2 compared to target mast

1 , but the general way in that the speed up factors change with wind direction is similar between the

two target masts: There is peak at about 30 deg and the speed up becomes smaller the more the wind

turns towards east. For wind within the direction range 180 deg to 360 deg, the difference between the

two target masts is even more pronounced, in that also the general form of the speed-up function

versus wind direction changes and not only the scaling.

At target mast 2 there is strong increase of the speed-up factor all through the wind direction range

from 195 deg to 315 degree. There is hardly any change of the speed-up factor for wind directions in-

between 315 deg and 345 deg. The increase is very characteristic for the site and ranges from 104.4% at

195 deg up to 128.5% at 345 deg. At target mast 1, this systematic direction dependent increase is not

visible at all. Instead, the speed-up factors become smaller for increasing wind directions greater than

180 degree.



Wind Direction

Sector Centre

[deg]

Number of

Data Sets

Reference

Mast

Target Mast

1

Target Mast

2

Mean Wind Speed at

Target Mast 1 / Mean

Wind Speed at Ref

Mast

Mean Wind Speed at

Target Mast 2 / Mean

Wind Speed at Ref

Mast

0 834 3.09 3.40 3.96 1.1004 1.2815

15 1253 3.57 4.16 4.78 1.1653 1.3399

30 1743 4.89 5.74 6.49 1.1744 1.3289

45 1229 4.16 4.75 5.66 1.1407 1.3600

60 1026 4.24 4.61 5.57 1.0872 1.3122

75 1105 4.88 5.02 5.96 1.0291 1.2211

180 2675 6.06 7.73 6.72 1.2749 1.1092

195 2309 5.42 6.71 5.66 1.2371 1.0441

210 2131 5.63 7.03 5.94 1.2473 1.0538

225 1791 5.94 7.37 6.41 1.2397 1.0791

240 1356 4.74 6.00 5.34 1.2673 1.1277

255 1605 4.96 6.15 5.69 1.2403 1.1471

270 2240 5.08 6.19 5.89 1.2196 1.1603

285 2112 5.68 6.87 6.81 1.2082 1.1980

300 1243 5.31 6.04 6.46 1.1389 1.2167

315 929 4.96 5.94 6.31 1.1980 1.2733

330 975 4.83 5.62 6.17 1.1649 1.2775

345 698 3.78 4.19 4.86 1.1077 1.2853

Mean Wind Speed [m/s]

Table 11: Sector-wise mean wind speeds (calculated from time series directly) and according speed-up

factors.



1

1.1

1.2

1.3

1.4

0 45 90 135 180 225 270 315 360

Spee

d-u

p F

acto

rv(T

arget

Mas

t1)

/ v(R

efer

ence

Mas

t) [

deg

]


Speed-up Factors

Target Mast 1Target Mast 2

Figure 86: Measured sector-wise speed-up factors (Ratio of sector-wise mean wind speed each target mast to

according mean wind speed at reference mast.



8.1.3 Turbulence Intensity

Mean turbulence intensity depending on wind speed is reported for the three masts in Figure 87 to

Figure 90. There is hardly any difference in the turbulence intensity for different wind speed classes

between the two target masts. The change of the turbulence intensity with wind speed is however

slightly different at the reference mast. On the one hand there is slight increase of mean turbulence

intensity with wind speed visible. On the other hand, for the anemometer that is used for the test at

the reference mast, turbulence intensity does not increase towards very low wind speeds. At the

second anemometer at the reference mast, that is not used for the test, turbulence does increase only

slightly for low wind speeds. In general, one may conclude from the reported turbulence behaviour that

turbulence properties at the reference mast are slightly different from those at the two target masts,

who are essentially the same. There is one second important site property regarding turbulence: Figure

91 and Figure 92 report the change of turbulence intensity with wind direction for all wind speeds

(Figure 91) and wind speeds between 8m/s and 10m/s (Figure 92). Both figures show strong increase of

the turbulence intensity with increasing wind direction for the wind direction range 180 deg to 360 deg,

i.e. turbulence intensity is much higher for wind from west than from south and much higher for wind

from north than from west. Turbulence intensity increases from about 7% to about 14% within this

range, considering the wind speed interval 8m/s to 10m/s.



0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Turb

ule

nce

Inte

nsi

ty [

%]

Num

ber

of

Dat

a in

Win

d S

pee

d C

lass

Wind Speed Class Centre [m/s]

Turbulence Intensity: Reference Mast

Figure 87: Turbulence intensity at the reference mast.



0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 17 0

1000

2000

3000

4000

5000

6000

7000

Turb

ule

nce

Inte

nsi

ty [

%]

Num

ber

of

Dat

a in

Win

d S

pee

d C

lass


Turbulence Intensity: Target Mast 1

Figure 88: Turbulence intensity at target mast 1.



0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 0

1000

2000

3000

4000

5000

6000

7000

8000

Turb

ule

nce

Inte

nsi

ty [

%]

Num

ber

of

Dat

a in

Win

d S

pee

d C

lass


Turbulence Intensity: Target Mast 2

Figure 89: Turbulence intensity at target mast 2.



0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 17

Turb

ule

nce

Inte

nsi

ty [

%]



Reference Mast (Used Anemometer)Reference Mast (Thies Classic)

Figure 90: Turbulence intensity at the three masts.



5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 45 90 135 180 225 270 315 360

Turb

ule

nce

Inte

nsi

ty [

%]

Wind Direction Sector Class Centre [deg]

Turbulence Intensity versus DirectionAll Wind Speeds

Reference MastTarget Mast 1Target Mast 2

Figure 91: Change of turbulence intensity with wind direction at all three masts.



5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 45 90 135 180 225 270 315 360

Turb

ule

nce

Inte

nsi

ty [

%]

Wind Direction Sector Class Centre [deg]

Turbulence Intensity versus DirectionWind Speed Interval [8m/s,10m/s[

Reference MastTarget Mast 1Target Mast 2

Figure 92: Change of turbulence intensity with wind direction at all three masts for wind speeds in the range from

8m/s to 10m/s.



8.1.4 Sector-wise Wind Speed Correlation Properties

The common time series of the three masts is analysed with respect to 30 degree wind direction

sectors, established on base of the wind direction measurement at the reference mast, excluding the

directions disturbed by the presence of the mast top. Scatter plots of the simultaneous wind speed

measurements at the reference mast and target mast 1 and target mast 2 are shown below in this

section. There is strong sector-wise correlation between the two target masts and the reference mast,

which varies slightly with wind direction (Figure 93). The correlation coefficient reaches its overall

maximum (regarding both masts and undisturbed sectors at the reference mast) at target mast 2 for

the 30 degree wind direction sector centred at 225 degree (r2=0.9324). For most sectors, correlation is

higher for the pair target mast 1 and reference mast than for the pair target mast 2 and reference mast.



0 5 10 15 20 25 300

5

10

15

20

25

30[0°, 30°[ r

2=0.89473

Wind Speed at Reference Mast [m/s]

Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[30°, 60°[ r

2=0.91825


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[60°, 90°[ r

2=0.84052


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[150°, 180°[ r

2=0.91719


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[180°, 210°[ r

2=0.92761


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[210°, 240°[ r

2=0.93236


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[240°, 270°[ r

2=0.87348


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[270°, 300°[ r

2=0.84289


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[300°, 330°[ r

2=0.87442


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[330°, 360°[ r

2=0.89486


Win

d S

pe

ed

at

Ta

rge

t M

ast

1 [

m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[0°, 30°[ r

2=0.84079


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[30°, 60°[ r

2=0.88916


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[60°, 90°[ r

2=0.8398


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[150°, 180°[ r

2=0.89335


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[180°, 210°[ r

2=0.89579


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[210°, 240°[ r

2=0.87557


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[240°, 270°[ r

2=0.86067


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[270°, 300°[ r

2=0.87258


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[300°, 330°[ r

2=0.87449


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[330°, 360°[ r

2=0.86882


Win

d S

pe

ed

at

Ta

rge

t M

ast

2 [

m/s

]



15 45 75 105 135 165 195 225 255 285 315 3450.82

0.84

0.86

0.88

0.9

0.92

0.94Correlation Coefficient versus Wind Direction

Wind Direction Sector Centre at Reference Mast [deg]

Corr

ela

tion C

oeffic

ient r2


Figure 93: Correlation coefficient versus wind direction.



8.1.5 Filling of a Gap in Target Mast 1 Data

In the common time series (common time steps) of the reference mast and the two target masts there

is no wind speed available for the used anemometer at target mast 1 for the period 2005-11-26

01:30:00 until 2005-12-19 14:50:00. It was decided that it is most appropriate for the purpose of the

test to fill this gap my means of sector-wise correlation with the anemometer at 60m height at the

same mast, du to the following reasons: Firstly, it is desired to have for the test a time series as

complete in time as possible. Would we neglect that period, that period would have to be excluded also

from the other masts. Secondly, this type of correlation (MCP correlation) is a standard procedure for

wind energy application and shows (see below) excellent correlation between the two anemometers at

the same mast.

The correlation procedure applied consists of linear regression in 36 10 degree wind direction sectors

using the regression function y=a*x+b. All data sets with wind speeds below 1m/s were filtered out

before the correlation function was fitted.

The total period 2005-11-26 01:30:00 until 2005-12-19 14:50:00 consists of 2319 time steps and

constitutes 7.1% of the total common period of the three masts.

The correlation coefficient r squared varies from sector to sector between 0.9551 and 0.9955. The

number of data in each 10 degree wind direction sector considered is sufficiently high. In order to check

the accuracy of the MCP correlation, a self consistency test was conducted. In that test, the regression

functions are applied to the data of the 60m anemometer for the common period of the 60m sensor

and the 80m sensor, i.e. for the period where there is wind speed data available from both

anemometers plus the direction data. The calculated wind speed time series for the 80m anemometer

is then compared to the measured one with respect to mean wind speed. This self consistency test

showed an error in mean wind speed of only 0.01%, indicating very good correlation.



8.1.5.1 Sector-wise Scatter Plots with Regression Lines

0 5 10 15 20 25 300

5

10

15

20

25

30[0°, 10°] r

2=0.98532 N=472

Wind Speed at 60m height [m/s]

Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[10°, 20°] r

2=0.99079 N=741


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[20°, 30°] r

2=0.99376 N=1004


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[30°, 40°] r

2=0.99545 N=1022


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[40°, 50°] r

2=0.9919 N=889


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[50°, 60°] r

2=0.97043 N=702


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[60°, 70°] r

2=0.96918 N=789


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[70°, 80°] r

2=0.9853 N=757


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[80°, 90°] r

2=0.98899 N=618


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[90°, 100°] r

2=0.96419 N=502


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[100°, 110°] r

2=0.95513 N=458


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[110°, 120°] r

2=0.96819 N=447


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[120°, 130°] r

2=0.97958 N=269


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[130°, 140°] r

2=0.97343 N=233


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[140°, 150°] r

2=0.98461 N=245


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[150°, 160°] r

2=0.99162 N=419


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[160°, 170°] r

2=0.99519 N=862


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[170°, 180°] r

2=0.98891 N=1267


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[180°, 190°] r

2=0.98886 N=1151


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[190°, 200°] r

2=0.99034 N=940


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[200°, 210°] r

2=0.99365 N=1224


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[210°, 220°] r

2=0.99345 N=1485


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[220°, 230°] r

2=0.99477 N=1436


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[230°, 240°] r

2=0.99157 N=1132


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[240°, 250°] r

2=0.99073 N=1414


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[250°, 260°] r

2=0.98799 N=1153


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[260°, 270°] r

2=0.98875 N=1107


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[270°, 280°] r

2=0.98863 N=1363


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[280°, 290°] r

2=0.99206 N=1197


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[290°, 300°] r

2=0.988 N=689


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[300°, 310°] r

2=0.98696 N=591


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[310°, 320°] r

2=0.99233 N=664


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[320°, 330°] r

2=0.99385 N=543


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[330°, 340°] r

2=0.99203 N=529


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[340°, 350°] r

2=0.98943 N=422


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[350°, 360°] r

2=0.9831 N=410


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



8.1.6 Filling of a Gap in Target Mast 2 Data

For further explanations on the method used see section 8.1.5.

The period with missing wind speed data at target mast 2 reaches from 2005-12-28 06:10:00 to 2005-

12-28 09:00:00, consisting of 18 time steps. This period represents 0.05% of the total common period

of the three masts. All data sets with wind speeds below 1m/s were filtered out before the correlation

function was fitted.

The correlation coefficient r squared varies between 0.9715 and 0.9944. The number of data in each 10

degree wind direction sector considered is sufficiently high. The self consistency test showed an error in

mean wind speed of 0.02%, indicating very good correlation.



8.1.6.1 Sector-wise Scatter Plots with Regression Lines

0 5 10 15 20 25 300

5

10

15

20

25

30[0°, 10°] r

2=0.98626 N=405


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[10°, 20°] r

2=0.98674 N=631


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[20°, 30°] r

2=0.99413 N=1008


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[30°, 40°] r

2=0.98917 N=1439


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[40°, 50°] r

2=0.97922 N=1241


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[50°, 60°] r

2=0.97186 N=865


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[60°, 70°] r

2=0.9903 N=706


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[70°, 80°] r

2=0.98893 N=773


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[80°, 90°] r

2=0.98307 N=708


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[90°, 100°] r

2=0.97149 N=526


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[100°, 110°] r

2=0.98749 N=590


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[110°, 120°] r

2=0.98669 N=420


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[120°, 130°] r

2=0.98869 N=310


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[130°, 140°] r

2=0.98973 N=292


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[140°, 150°] r

2=0.99016 N=363


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[150°, 160°] r

2=0.99409 N=682


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[160°, 170°] r

2=0.98745 N=1313


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[170°, 180°] r

2=0.98657 N=1898


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[180°, 190°] r

2=0.99052 N=1514


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[190°, 200°] r

2=0.99378 N=1153


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[200°, 210°] r

2=0.99428 N=1066


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[210°, 220°] r

2=0.99439 N=1233


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[220°, 230°] r

2=0.99253 N=1311


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[230°, 240°] r

2=0.99323 N=1161


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



0 5 10 15 20 25 300

5

10

15

20

25

30[240°, 250°] r

2=0.98685 N=994


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[250°, 260°] r

2=0.98601 N=996


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[260°, 270°] r

2=0.98585 N=1115


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[270°, 280°] r

2=0.99106 N=1139


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[280°, 290°] r

2=0.99227 N=1577


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[290°, 300°] r

2=0.99259 N=2029


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[300°, 310°] r

2=0.98793 N=1431


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[310°, 320°] r

2=0.99256 N=906


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[320°, 330°] r

2=0.99086 N=711


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[330°, 340°] r

2=0.99286 N=652


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[340°, 350°] r

2=0.99266 N=538


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]

0 5 10 15 20 25 300

5

10

15

20

25

30[350°, 360°] r

2=0.98387 N=430


Win

d S

pe

ed

at

80

m h

eig

ht

[m/s

]



8.2 Issues related to the site selection process

This section outlines the experience collected during the analysis of many wind measurements that

were taken into account during the section of the test site.

Some of the requirements listed in Table 1 are easy to check. Features like the terrain complexity or the

presence of calibrated anemometers are quickly verifiable. Ownership issues, on the other hand,

required negotiations. The project and its principles were presented to the company owning rights on

the measurements data-set and the owner had to consider the possibility of accepting that the data are

used for the project. Other features are much more difficult to investigate and often required a

preliminary CFD simulation of the wind conditions in order to confirm the flow features recorded by the

measurements.

8.2.1 General Considerations

In very complex terrain, wind measurements pose high demands and it is often difficult to reach the

high standards in all points. Complex terrain often means very difficult accessibility which can pose

problems to the transport of high quality measurement mast equipment. Up in the mountains, weather

conditions can be extreme, which often leads to problems with lightning strokes, low temperature,

icing, rain and snow and severe storms. Power supply for the logger and earthing can be difficult here.

Space for mast erection may be very limited and the anchoring of the guy wires can be difficult. We

observed major problems related to measurement equipment hit by lightning strokes. At specific

locations, this can make a wind measurement almost impossible.

In general, a significant share of wind measurements that we have to assess in the context of wind

resource assessments has major shortcomings regarding the measurement technique. Examples are:

- uncalibrated anemometers;

- anemometer calibration performed by laboratory that doesn’t have any traceable certification;

- lack of a top-mounted anemometer combined with much too short booms;

- mounting of anemometers directly on the boom without any distance tube;

- inappropriate alignment of the booms with respect to main wind directions;



- incomplete or missing measurement documentation (regarding erection, operation and

dismantling). In fact, a complete measurement documentation is rarely present;

- inappropriate lightning protection (too close to sensor, diameter too large);

- wind vane mounted on a too short boom;

- top anemometer mounted too close to the mast top;

- wrong or unknown logger configuration.

It is true that most wind measurements shown one or more of these shortcoming. Although the

situation appears to become better with recent measurements we had to exclude those measurements

from the beginning, accepting that this means a major reduction of the sites considered.

8.2.2 Wind Speed Measurement

High quality demands are posed to the wind speed measurements with cup anemometers.

Major problems with wind speed measurements in complex terrain can be related to inclined flow and

turbulence. Different anemometer types tend to respond differently on inclined flow, relating both to

mean, terrain induced inclination and fluctuating inclination attributed to turbulence. Wind tunnel

calibrations refer to steady, laminar flow. On site, however, the wind approaches the sensor also from

above and below, depending on the terrain, the turbulence intensity and the ratio of horizontal to

vertical turbulence. These turbulence conditions can change with position in complex terrain, so it

cannot simply be assumed that the effect is the same at two masts equipped with the same

anemometer type. While the turbulence intensity can be derived from a cup anemometer

measurement, the ratio of horizontal to vertical turbulence would require a sonic anemometer, which

is very rarely used.

The strongest inclination effects are observed if one compares measurements obtained with the Thies

Classic anemometer type with other anemometers. The Thies Classic anemometer measures „more

wind“, compared to a Thies First Class anemometer, because is tends to respond to the magnitude of

the flow vector and not to its horizontal projection.

It is not viable to give to the participants reference data measured with a Thies Classic and compare

calculated wind conditions to those measured with a Thies First Class anemometer, because a large part

of the wind speed difference between the two (up to several percent) must solely be attributed to the

dynamic outdoor behaviour of the anemometers.



As we do not have systematical studies at hand about the dynamic outdoor behaviour of the NRG

anemometers and because we are not fully convinced of the sensor performance, we had to exclude

those from the test and, as a result, we had to exclude a major part of complex terrain wind

measurements worldwide, in particular in Spain.

Finally, one can of course get direction dependent inclination effects if the mast is not erected strictly

vertically or the sensor is not mounted vertically. It can be difficult to separate that from terrain-

induced flow inclination and other effects.

8.2.3 Spatial Arrangement

Wind measurements in complex terrain are not designed for the purpose of model verification. In

general, the spatial arrangement of many measurements aims at minimising distances between masts

and turbines. Often, single wind measurements are to be used for energy yield calculations only on the

same hill/ridge, as one is aware that the uncertainty of extrapolation from one hill to a nearby hill is

associated with considerably higher uncertainties than the extrapolation on the same hill/ridge. As a

result, the spatial arrangement of measurement masts in complex terrain is seldom satisfactory for

purposes of model verification, where one wishes to have several masts on the same hill/ridge.

8.2.4 Wind Direction Measurements

We know that the wind conditions in complex terrain strongly depend on wind direction, especially if

the site is located on a high, steep ridge. On the other hand, we found that very often there are

problems related to the wind direction measurement. In general, the main focus has been on the wind

speed measurement and the interest in the wind direction measurement has been less intensive. In our

opinion, this is a major shortcoming.

Wind direction errors that we observed, are related to different sources:

It is not always known with a high enough certainty to what direction the north mark of the wind vane

points. Comparisons of wind direction measurements at nearby masts showed in many cases that there

is, on average, a constant wind direction offset between the two. Terrain-induced wind direction

changes, on the other hand, tend to be strongly direction dependent and can not be made responsible

for a constant offset. With only two masts present at a site and the lack of backup wind vanes, there is



no way to identify, if there is a problem with the north mark alignment at one of the masts (or even on

both). The offset cannot be corrected.

There are different methods to determine the direction of the north mark. It may be aligned with the

boom and the boom direction may be measured later with a compass or a GPS device some distance

away from the mast, possibly using binoculars. It is possible that this method has major shortcomings in

very complex terrain, where one cannot easily reach any desired point around the mast and the view

can be restricted by topography. For specific cases, this method is not applicable at all due to still

ongoing demining activities in an area of south-east Europe. If the direction of the boom/north mark

has been determined with a compass, one has to transform that direction using the magnetic

declination. This declination is moderate (<10°) in central Europe and can reach considerable values in

other parts of the world. It happens that after the measurement mast has been dismantled, it can be

difficult to identify at all if the north mark direction has been determined with or without application of

the declination. Another problem with wind direction measurements is related to the wind vane type

and its mounting. Although we can not yet prove it, we put forward the hypothesis that in many cases

the wind direction measurement is significantly disturbed by the mast and by possible other equipment

that may be mounted on the same boom than the wind vane. This disturbance is caused by flow

direction changes that are induced by the pressure field developing around the mast. While it is clear

that the wind vane is exposed to higher direction fluctuations in the wake of the mast, we suppose that

especially at inflow angles around 45° /135° to the vane boom there are systematical, direction

dependent errors in the wind direction measurement.



Figure 94: Sketch of the flow around a mast to illustrate our hypothesis of an influence that the mast can have on the

wind direction measurement. In this case the measured wind direction is turned by a few degrees clockwise with

respect to the ambient undisturbed mean wind. The effect should be the more pronounced the smaller the ratio of

boom length to mast diameter is and it should have its maximum if the flow is at about 45°/135° to the boom.

In practice, we sometimes found it impossible to decide if a particular wind direction effect observed

between two mast must be attributed to the terrain or to such a small-scale dynamic effect.

Unfortunately, this was the case for the most interesting site that was initially considered for the test

(see Figure 95). For another site, there was a strange wind direction effect which could not be

explained at all (see Figure 96).

We decided to exclude those sites, because we were, in the given frame, not able to state for sure if it

was a terrain induced wind direction effect or an effect that is caused by the mast itself.



8.2.5 Exemplary illustrations of some problematic effects found during site search

In this section a number of images with explanations are collected that shall give a representative

overview of the problematic effects that we observed during our site selection work. All sites are

anonymous. Also, it is not specified, what companies were operating these measurements.

We show only the scatter plots and not the quantitative, bin-averaged results, because the images shall

only illustrate the problems that we faced during our work. For this purpose, we find it more useful to

display the data itself and not only average values.

One must keep in mind that all measurements described below do perfectly conform to current IEC and

IEA standards. All anemometers are calibrated according to MEASNET standard.

Figure 95: Site XX1: Wind direction scatter plot for two nearby met masts in very complex terrain. Wind directions are

centred around north (so e.g. –30° corresponds to 330°). There is a direction independent offset and a direction

dependent offset for wind from NW. Note that if this would be an effect from the mast, it should basically be

symmetric around the boom direction. In this case however, the vane booms at the two masts are aligned to

different directions.



Figure 96: Site XX2: Wind direction scatter plots for two nearby met masts in very complex terrain. Wind directions

are centred around north (so e.g. –30° corresponds to 330°). There is a strange behaviour for wind for NNE. Above: All

wind speeds, below: Wind speed at ref1 between 4 m/s and 8 m/s. Note that both vanes are potentiometric wind

vanes and that the „singularity“ can not be attributed to a possible averaging problem related to the „north-jump“.

The terrain to the NNE of the masts is of only moderate complexity.



Figure 97: Site XX3: Wind direction scatter plot for two nearby met masts in moderately flat terrain. There is a

direction dependent offset that reaches its maximum for wind directions of about 120°. We did not have a

satisfactory explanation for this behaviour.



Figure 98: Site XX4: Wind direction scatter plot for two nearby met masts in complex terrain. There is a direction

independent offset.



Figure 99: Site XX5 and XX6: Scatter plots of the ratio of wind speed measured with a top-mounted anemometer to

that measured with a backup anemometer vs. wind direction. The top anemometers are of type Risoe, the backups

are of type Vector. The measurement installations fully conform to IEC and IEA standards. The backup anemometer is



mounted on a boom pointing to 260° (above) and 251° (below), lightning protection of the top anemometer is at 80°

(above and below). No other disturbances of the sensors are expected. Vertical distance between top and backup

anemometer rotor planes is 3.3 m. A direction dependent effect in the image above, most visible from 160° to 300°, is

not easily explained. Also, the comparably large mean difference between top and backup in the image below is

difficult to explain.

8.2.6 Maps

Other, major problems are related to geographic issues.

More often than one would expect, the situation regarding high quality, high resolution up-to-date

topographical maps is not satisfactory. An example is the area of Turkey. There were extensive high

quality wind measurement campaigns in complex terrain in this area and we initially spent much time

to assess those we had access to. However, finally we were not able to obtain topographical maps of a

high enough standard. The reason is mainly the role that the military plays in the management of

geographic information in this area. High resolution up-to-date topographic data was not readily

available to us. The topographical information plays an important role in the test. It is a matter of fact

that one can in many cases have topographical information of higher and higher quality, depending on

how much money one is able to spend. We decided that it is reasonably if we demand that the quality

of the maps should be comparable to what is commonly used in wind resource assessment work in

central Europe (up-to-date topographical maps in the scale 1:25000 or 1:5000, possibly supplemented

by aerial photos, for large distances from the site possibly completed by SRTM data). For the site and

the area of several km around it we consider SRTM data as having a much too high uncertainty.

8.2.7 Forest

Modelling effects of forest on the wind flow is a major challenge by itself. Although there are „forest

models“ that can be attached to the flow models, we decided to exclude forest effects from the test as

far as possible. The difficulties of forest modelling are manifold: First of all, one has to quantify exactly

what is meant with „forest“, i.e. its height, type, density and extent has to be assessed. This information

is not readily available. Secondly, forest tends to grow. There are trees, as the very common eucalyptus

trees on the Iberian Peninsula, that can grow by a significant amount even during a measurement

campaign so that one would have to consider that in some way. Such fast growing trees also tend to be

subject to extensive felling activities, making it difficult to exactly specify the extent at all times. Lastly,



the flow structures that develop in the lee of a forest boundary have a smaller spatial scale than the

remaining flow. In the luv of a forest boundary there may be flow retardation. The modelling of those

flow features poses its own demands (grid refinement, turbulence model or empirical correction) and

has a different uncertainty attached to it. We do not want to mix model uncertainties caused by

incorrect modelling of complex terrain flow and by forest modelling. We excluded sites where there is

significant forest close to one of the measurement masts.

8.2.8 Coordinates

Today, mast coordinates are taken by GPS, which has an acceptable accuracy if properly used. The

number of satellites found by the device can be restricted by topography, leading to a higher

uncertainty of the position measurement. Sometimes GPS coordinates can be immediately verified on

site as being wrong, for some unknown reason. Also, in many cases the mast coordinates are not

recorded in the coordinate system of the map for that area. Specifically, GPS coordinates are often

recorded in geographical coordinates with a WGS84 reference frame. Topographical maps can have a

number of different coordinate systems, which are sometimes not obvious (e.g. for the area of Bosnia

and Herzegovina). For simplicity, we decided to supply all maps and coordinates in the UTM WGS84

system. Therefore, maps and coordinates have to be reprojected to that system. Specifically, paper

maps have to be scanned, georeferenced, reprojected and vectorized. This has to be done with great

care in complex terrain and can take considerable time. Having done all that, one may nevertheless

finally find that the position of the mast in the projected map does not fully conform to the visual

impression that one has gained on site. If one can confirm the correctness of the GPS measurement and

the map and projection work, one is left with the statement that the topographical map is not accurate

for that area. In those cases did we have to exclude the site from the test.



8.2.9 Data from Numerical Site Calibrations

If power curves of wind turbines shall be measured according to IEC standard and the terrain on site is

„too complex“ according to IEC specifications, a measured site calibration must be carried out before

the turbine is erected. For that, a wind measurement at the planned turbine position at hub height is

installed (WTGS mast). Additionally, a wind measurement is installed at hub height at the position of

the mast that shall later be used for the power curve measurement. Both measurements are operated

until enough data in the expected power curve measurement sector is collected.

These site calibrations have to follow very high and restrictive standards and therefore give highly exact

results. Accordingly, those measurements are excellent candidates for Round Robin test sites. DEWI has

carried out a large number of those site calibrations.

This is the theory.

In reality, those measurements are not that easily accomodated in the test, due to the following

reasons:

- At the WTGS mast, that is erected at the later turbine position, there is no requirement for a wind

direction measurement and in most cases there isn’t one. If we would give to the participants the

wind speed and direction data of the reference mast and let them calculate for the site mast, we

could only compare the wind speed results and not the accuracy with which the models can model

the wind direction changes in complex terrain. For any direction dependent assessment of site mast

predictions, one would have to assume no change between reference and site mast. Looking at the

usually small distances between reference and site mast, this is a reasonable assumption, but it is

difficult to quantify the uncertainty of such an assumption.

- The site calibration and the power curve measurement is often restricted to only a small wind

direction sector, sometimes down to 30° or so. We would have to restrict the assessment to that

sector also. This restriction does however lower the practical relevance of the assessment, as the

real wind is not restricted to the measurement sector.

- The distance between the WTGS mast and the power curve measurement mast has to be

inbetween 2 and 4 rotor diameters, 2.5 rotor diameters is the recommended distance. This distance

does not correspond to typical distances for extrapolation of mast measurements to turbine

positions in wind resource assessments, except for single turbines in a farm.



- Site calibrations are operated until the number of data in specified classes exceed defined limits. In

practice, site calibrations are usually operated for comparably short periods that are much shorter

than one year (in most cases site calibrations are in operation for 2 up to 3 months. Due to possible

seasonal effects it is however desirable to have about one year of data for the test.

- If there is more than one site calibration in operation in the same area, one may consider to use

their combined data for the test, i.e. give to the participants data from one reference mast and let

them calculate for the other reference mast, possibly by discarding the site masts. But this is not

easily done, as the measurements are in general designed such that data is only used from within

the measurement sectors. For other directions there may be disturbances due to sensors, lightning

protection or obstacles like other wind turbines, trees, houses, forest, or similar. The test needs

however to be restricted to a measurement sector that is common to all site calibrations installed.

Such a common sector can be even smaller than the already narrow measurement sectors, if a

common sector is found at all. Additionally, the spatial distribution of different site calibrations in

an area does not necessarily represent typical locations of met masts and wind turbines in a

complex terrain wind farm project, i.e. the spatial arrangement can be quite unrealistic. The reason

is that site calibrations tend to be operated at the outer boundary of wind farms in order to get rid

of the turbine wakes in the measurement sectors.

- By definition, site calibration measurements have to be done exclusively at hub height. There are

recent developments regarding the additional use of wind speed measurements at the height of

the lower blade tip. As a result, these measurements give in general no reference for a test

regarding the model‘s performance on vertical extrapolation. Wind measurements for the purpose

of energy yield assessment studies do rarely reach hub height, although this has changed a bit now.

So the accuracy with which the models can extrapolate vertically should be part of the test.

- In order to separate model uncertainty from measurement uncertainty, one wishes to have site

calibration factors as different from 1 as possible. There are ongoing site calibrations at extreme

sites. There measured site calibration factors can well exceed 110%. But along with increasing

factors also the scatter in the data increases and the meaning of an average factor is weakened.



8.3 Output data Format

The present section contains information about the data format of the results delivered to DEWI.

8.3.1 Mean Horizontal Wind Speed for terrain following surfaces - Data Format

TAB-separated ASCII file with one grid point in each line.

Format for each line (all coordinates in UTM WGS 84 Zone 32):

East-Coordinate North-Coordinate Mean Wind Speed [m/s].

- No leading space characters allowed at the beginning of the line.

- The new-line-characters must be „CR LF“ or 0D 0A (Hexadecimal).

- Floating point numbers for the wind speed.

- No header lines allowed.

Ordering of the lines starts with the minimum east and north coordinates. The north coordinate

increases first and the east coordinate remain fixed. When the north boundary is reached, the north

coordinate resets, the east coordinate increases and so on. The values were saved in the ASCII files:

XYZ.mean40.dat, XYZ.mean60.dat, XYZ.mean80.dat where XYZ is the participant-id string which has

been assigned before the start of the Dummy-Run. (es. ANM.mean60.dat)

8.3.2 Mean Wind Speed for target locations - Data Format

TAB-separated ASCII file with one grid point in each line. Format for each line:

Point-ID East-Coordinate North-Coordinate Mean Wind Speed [m/s]

- No leading space characters allowed at the beginning of the line.

- The new-line-characters must be „CR LF“ or 0D 0A (Hexadecimal).

- Floating point numbers for the wind speed.

- No header lines allowed.

The values were saved in the ASCII files:

XYZ.points40.dat, XYZ.points60.dat, XYZ.points80.dat

where XYZ is the participant-id string which has been assigned before the start of the Dummy-Run. Es.

ANM.points40.dat



8.3.3 Wind Statistics for target locations - Data Format

This wind statistic must be provided in the common WAsP „.tab” data format with 24 wind direction

sectors of 15° width each and wind speed intervals of 1 m/s each.

The data format for this statistics is described in the European Wind Atlas and Application Program web

page: http://www.risoe.dk/vea/projects/nimo/WAsPHelp/FileFormatofTAB.htm

The specification of the coordinates and the height of the location in the tab file were not required and

they had been not be taken into account in the evaluation. Nevertheless, in order avoid any confusion

and to create tab-files conform to the WAsP program, the participant were asked to assign longitude

and latitude equal to 1. Values were saved in the tab files: XYZ.point-id.tab

where XYZ is the participant-id string which has been assigned to each participant before the start of

the Dummy-Run and point-id is the identification string relative to each point.

Additionally the participant were asked to provide 24 separate statistics. The format for these statistics

was the same as requested for the whole time-series. Since it was probable that for these statistics only

few wind sectors contain data, the participant were requested to define empty sectors by setting to 0

the sector-wise frequency of occurrence for those empty sectors (in line 4 of the Tab file) and, for the

empty sectors, to set to 1000 the frequency of the wind speed interval ranging from 0 m/s to 1 m/s.

The remaining frequencies in the empty sector should be set to zero as in the example in Table 12.



No Description |

1.00 1.00 1.0

12 1.00 0.00

7.89 0.98 0.71 0.00 2.72 8.93 11.73 9.09 4.81 1.90 3.59 47.65

1.00 10.77 111.11 83.48 1000.00 34.44 9.95 5.58 8.42 17.46 55.81 27.17 1.99

2.00 96.21 368.69 283.48 0.00 162.21 77.11 46.19 46.05 62.13 186.89 144.77 18.96

3.00 168.01 291.67 286.96 0.00 261.89 179.24 77.54 39.26 81.64 204.41 148.56 38.55

4.00 136.24 131.31 160.00 0.00 222.02 216.69 90.06 53.38 100.13 158.34 144.09 48.66

5.00 123.32 54.29 128.70 0.00 122.79 200.66 83.32 82.45 116.56 134.33 105.91 63.47

6.00 115.42 12.63 46.96 0.00 80.65 122.31 100.58 93.59 121.44 110.97 90.78 73.55

7.00 108.96 12.63 10.43 0.00 71.14 77.11 117.20 107.44 143.52 76.57 65.34 72.80

8.00 82.39 13.89 0.00 0.00 29.00 39.66 114.26 119.80 120.92 37.64 57.43 68.68

9.00 47.03 3.79 0.00 0.00 15.86 25.43 93.00 128.50 91.40 13.63 37.48 72.44

10.00 20.10 0.00 0.00 0.00 0.00 16.03 89.85 95.08 54.17 5.84 22.70 71.40

11.00 30.52 0.00 0.00 0.00 0.00 8.02 67.23 90.19 41.34 5.19 25.10 67.96

12.00 13.82 0.00 0.00 0.00 0.00 7.05 53.66 63.71 29.78 9.09 18.57 67.80

13.00 14.18 0.00 0.00 0.00 0.00 4.84 22.20 40.34 13.09 0.00 17.88 60.24

14.00 15.44 0.00 0.00 0.00 0.00 5.11 14.31 19.56 4.36 1.30 13.07 52.75

15.00 7.36 0.00 0.00 0.00 0.00 5.80 9.89 8.15 0.51 0.00 11.00 43.55

16.00 3.41 0.00 0.00 0.00 0.00 3.04 8.94 2.58 1.03 0.00 13.07 38.47

17.00 2.33 0.00 0.00 0.00 0.00 1.38 3.58 1.36 0.00 0.00 9.97 30.88

18.00 2.33 0.00 0.00 0.00 0.00 0.28 2.31 0.00 0.00 0.00 7.91 23.78

19.00 0.36 0.00 0.00 0.00 0.00 0.00 0.32 0.14 0.00 0.00 13.76 22.28

20.00 0.18 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.51 0.00 11.69 15.88

21.00 1.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.22 11.79

22.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.85 10.23

23.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 7.49

24.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 3.39

25.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.12

26.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.11

27.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.76

28.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.45

29.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.98

30.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57

31.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41

32.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21

33.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10

34.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26

35.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Table 12: Example TAB file with the 4th

sector empty.



9 References

[1] Troen, E.L. Petersen: European Wind Atlas. Risø National Laboratory, Roskilde, Denmark, 1990.

[2] G. Mortensen, L. Landberg, I. Troen, E.L. Petersen: Wind Atlas Analysis and Application Program

(WASP), Risø National Laboratory, Roskilde, Denmark, 1993 and updates.

[3] MEASNET: Power Performance Measurement Procedure, 3rd Ed., November 2000.

[4] IEA: IEA Recommendation 11: Wind Speed Measurement and Use of Cup Anemometry, 1st Ed.,

1999.

[5] International Electrotechnical Commission (IEC): IEC61400-12-1 Wind turbines - Part 12-1: Power

performance measurements of electricity producing wind turbines, 1st ed., 12/2005.

[6] A. Albers, H. Klug, D. Westermann: Outdoor comparison of cup anemometers, proceedings of

DEWEK 2000, DEWI, Wilhelmshaven.

[7] A. Albers, H. Klug: Open Field Cup Anemometry, Proceedings of European Wind Energy Conference

2001, Kopenhagen, Denmark.

[8] IEC: IEC61400-1 Wind turbine generator systems - Part 1: Safety Requirements, 2nd Ed., 1998.

[9] IEC: IEC61400-1 Wind turbines - Part 1: Design Requirements, 3rd Ed. 08/2005.

round robin numerical flow simulation in wind energy - dewi

Documents