wind resource analysis report - final
TRANSCRIPT
Wind Resource Analysis
Final Report
December 2011
Project 07112
Prepared By:
Justin M. Harrell, P.E.
i
Acknowledgement This work was supported in part by a grant from the Illinois Clean Energy Community Foundation,
Request ID: 3867, Wind Turbine Feasibility Study.
Thanks are due to Brandon Banbury, undergraduate research assistant 2007-2008, for his help with the
study.
Acronyms AGL Above Ground Level
AMSL Above Mean Sea Level
ASOS Automated Surface Observing System
EMI Electro-Magnetic Interference
ESD Electro-Static Discharge
FAA Federal Aviation Administration
ICAO International Civil Aviation Organization
ICECF Illinois Clean Energy Community Foundation
IEC International Electrotechnical Commission
KMDH ICAO code for Southern Illinois Airport
MCP Measure-Correlate-Predict
METAR Meteorological Aviation Report
NWS National Weather Service
RMSE Root-Mean-Squared Error
RTD Resistance Temperature Detector
SIUC Southern Illinois University Carbondale
ii
Contents Acknowledgement ......................................................................................................................................... i
Acronyms ....................................................................................................................................................... i
Figures .......................................................................................................................................................... iii
Tables ........................................................................................................................................................... iv
Introduction .................................................................................................................................................. 1
Data Collection and Processing ..................................................................................................................... 1
WSIU Radio Tower .................................................................................................................................... 1
Instrumentation ........................................................................................................................................ 2
Data Logger ............................................................................................................................................... 4
KMDH Weather Data ................................................................................................................................ 5
Calculations ............................................................................................................................................... 7
Wind Power Density.............................................................................................................................. 7
Air Density ............................................................................................................................................. 8
Air Pressure ........................................................................................................................................... 8
Dew Point Temperature ........................................................................................................................ 9
Wind Shear ............................................................................................................................................ 9
Turbulence Intensity ........................................................................................................................... 10
Data Validation ........................................................................................................................................... 10
Wind Speed ............................................................................................................................................. 11
Wind Direction ........................................................................................................................................ 14
Temperature ........................................................................................................................................... 15
Humidity .................................................................................................................................................. 16
Pressure .................................................................................................................................................. 17
Final Data Set .......................................................................................................................................... 19
Wind Study Results ..................................................................................................................................... 21
Wind Speed ............................................................................................................................................. 24
Wind Direction ........................................................................................................................................ 25
Wind Power Density ............................................................................................................................... 26
Air Density Factor .................................................................................................................................... 27
Wind Shear Exponent ............................................................................................................................. 28
Turbulence Intensity ............................................................................................................................... 29
Analysis ....................................................................................................................................................... 30
KMDH Historical Reference Data ............................................................................................................ 30
Wind Speed Distribution for Estimating Annual Energy Production ...................................................... 31
Conclusion ................................................................................................................................................... 34
Suggestions for Future Research ................................................................................................................ 35
Appendix A .................................................................................................................................................. 37
Unit Conversions ..................................................................................................................................... 37
Data Logger Channels ............................................................................................................................. 37
References .................................................................................................................................................. 38
iii
Figures Figure 1: Location of Proposed Wind Turbine and WSIU Radio Tower Study Site ....................................... 2
Figure 2: WSIU radio tower with sensor mounting locations, view from the east. ...................................... 4
Figure 3: Ground level instrument cluster attached to south tower leg at 2m. ........................................... 4
Figure 4: 12-channel data logger installed in radio amplifier building ......................................................... 5
Figure 5: KMDH ASOS instrumentation array with Southern Illinois Airport in background ....................... 6
Figure 6: Wind Shadowing of Radio Tower at 102 m, 81 m, and 52 m Levels Based on Wind Direction ... 13
Figure 7: Variation between Site and KMDH Wind Vanes .......................................................................... 14
Figure 8: Comparison of Site Top, Site Ground, and KMDH Temperature Measurements ........................ 15
Figure 9: Histogram and Hourly Variation of Temperature Difference (Site Ground – KMDH) ................. 16
Figure 10: Comparison of Site and KMDH Dew Point Temperature ........................................................... 17
Figure 11: Comparison of Site and KMDH Pressure Measurements .......................................................... 17
Figure 12: Histogram and Hourly Variation of Pressure Difference (Site – KMDH) .................................... 18
Figure 13: Monthly Mean Wind Speed ....................................................................................................... 24
Figure 14: Distribution and Hourly Mean of Wind Speed for All Data ....................................................... 24
Figure 15: Monthly Energy Yield Fraction by Wind Direction ..................................................................... 25
Figure 16: Wind Roses for Frequency, 102 m Energy Yield, and 102 m Wind Speed Percentiles ............. 25
Figure 17: Monthly Mean Wind Power Density.......................................................................................... 26
Figure 18: Distribution and Hourly Mean of Wind Power Density ............................................................. 26
Figure 19: Monthly Mean Air Density Factor .............................................................................................. 27
Figure 20: Distribution and Hourly Mean of Air Density Factor ................................................................. 27
Figure 21: Monthly Mean Wind Shear Exponents ...................................................................................... 28
Figure 22: Distribution and Hourly Mean of Wind Shear Exponents .......................................................... 28
Figure 23: Monthly Mean Turbulence Intensity ......................................................................................... 29
Figure 24: Distribution and Hourly Mean of Turbulence Intensity ............................................................. 29
Figure 25: Annual Hours Observed at KMDH, 1973 – 2009 ........................................................................ 30
Figure 26: KMDH Annual Mean Wind Speed, Daytime Hours 7-18, 1973 – 2009 ...................................... 31
Figure 27: Average Monthly Distribution of Wind Speed, 102 m Level ..................................................... 32
Figure 28: Average Annual Distribution of Wind Speeds, Each Level, with Representative Wind
Turbine Power Curve and 102 m Yield Curve ........................................................................... 32
iv
Tables Table 1: Meteorological sensor specifications .............................................................................................. 3
Table 2: Symphonie data logger resolution .................................................................................................. 5
Table 3: ASOS sensor specifications and data characteristics ...................................................................... 7
Table 4: Elevations used in air pressure calculations for wind power and air densities .............................. 9
Table 5: Wind Vane Offset Determination ................................................................................................. 14
Table 6: Summary of Data Collected ........................................................................................................... 19
Table 7: Summary of Validation Results for Site Data ................................................................................ 19
Table 8: Summary of Signal Selections for the Final Data Set .................................................................... 20
Table 9: Summary Statistics for All Data ..................................................................................................... 21
Table 10: Monthly Mean ± Standard Deviation of Wind Speed and Wind Power Density, Prevailing
Wind Direction for Frequency and Wind Energy Yield ............................................................... 22
Table 11: Monthly Mean ± Standard Deviation of Air Density, Pressure, Temperature, Dew Point,
Wind Shear Exponent, and Turbulence Intensity ....................................................................... 23
Table 12: Average Annual Wind Speed Frequency Distributions ............................................................... 33
1
Introduction In 2007, Southern Illinois University Carbondale (SIUC) initiated a study of the campus wind resource as
part of a feasibility study for a proposed project to install a large-scale wind turbine to generate
electricity for use on campus. Previous point wind resource studies had been completed for sites in
Pulaski, Franklin, and Hamilton Counties through the Illinois Wind Monitoring Program[1]
, however the
wind resource is highly dependent on local terrain and measurement height, and none of these sources
of data was sufficient to justify or preclude investment in wind power at SIUC. In order to determine the
viability of a wind turbine installation at SIUC, a detailed feasibility study was required both to quantify
the local wind resource and to determine other parameters essential to a successful wind turbine
installation.
Electric rate increases since 2007 following the lifting of the legislated 10-year rate freeze and a change
in the rate structure that removed the prior emphasis on peak demand charges favor increased
investment in projects to reduce purchased electric energy. Coupled with SIUC’s commitment to
improve campus sustainability, the potential for wind-energy-related student research and training, the
ability to diversify the campus power supply and create a hedge against future energy market volatility,
and with potential grant incentives, these changes provided an ideal opportunity to develop a wind
energy generation project on campus.
The purpose of this report is to describe the activities and findings of the wind resource study conducted
on the SIUC campus from Nov. 2007 through May 2010. Determination of the ultimate feasibility of a
wind energy project on campus is beyond the scope of this report, but these results will be instrumental
in selecting a wind turbine that can perform well at this location. It is hoped that this study and report
will add to the knowledge of the Illinois wind power resource, especially in southern Illinois, and that
other wind project developers can benefit from knowledge of the process and results of the study.
Data Collection and Processing
WSIU Radio Tower
The WSIU radio tower was chosen as the site for the wind resource study because of its height and
proximity to the proposed location of the wind turbine. The radio tower is located on the SIUC campus,
between Campus Lake and McLafferty Rd. at 1450 Radio Dr. The study site coordinates are 37° 42’
29.39” N and 89° 14’ 6.90” W. The proposed wind turbine site is located 847 m (2780 ft) to the
southwest (236°) from the radio tower, west of McLafferty Rd. and 262 m (860 ft) north of the SIUC
Vermicomposting Center, located at 3373 W Pleasant Hill Rd. The coordinates for the wind turbine
location are 37° 42’ 14.04” N and 89° 14’ 35.52” W. Figure 1 shows the locations of the proposed wind
turbine and radio tower. The radio tower’s base elevation is 142 m (466 ft) above mean sea level
(AMSL) and the tower height is 102 m (336 ft) above ground level (AGL). The wind turbine site elevation
is 450 ft AMSL and the turbine hub height is expected to be 100 m (328 ft) AGL. The proximity to the
proposed turbine site and the ability to monitor wind speeds at the proposed hub height make the WSIU
radio tower the ideal location to study the wind resource available for energy production.
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The tower is used primarily for broadcasting the WSIU FM radio signal from a series of six omni-
directional antennas mounted up and down the east side of the tower between the heights of 85 –
102 m (280 – 336 ft). The University also leases space on the tower to three cellular telephone
companies, whose antennas are located at approximately 20 m (66 ft), 35 m (114 ft), and 55 m (182 ft).
Other miscellaneous antennas are located at heights ranging from 64 – 77 m (210 – 252 ft).
The tower, constructed in 1958, is self-supporting and consists of 14 triangular sections with cross-
braced faces, each of 7.3 m (24 ft) height. The three tower faces are oriented approximately west
(270°), south-southeast (150°), and north-northeast (30°). The tower tapers so that the face width
ranges from 7.1 m (23 ft) at the base to 1.2 m (4 ft) at the top. The top section has vertical legs. Tower
height and structural data were obtained from a structural analysis report by GEM, Inc., 2004.[2]
Figure 1: Location of Proposed Wind Turbine and WSIU Radio Tower Study Site
Instrumentation
On October 24, 2007, meteorological instrumentation was purchased from NRG Systems, Inc. of
Hinesburg, VT and was installed on the tower on November 7, 2007, by ABHE & Svoboda, Inc. of Prior
Lake, MN. SIUC personnel completed the ground level wiring and commissioned the system on
November 19, 2007.
The instrumentation included a total of 12 sensors, associated mounting hardware, and a 12-channel
data logger. The sensors included six, 3-cup anemometers for measuring wind speed, two rotating
vanes for measuring wind direction, two integrated-circuit air temperature sensors with solar radiation
shields, one absolute pressure sensor, and one relative humidity sensor. Temperature, pressure, and
humidity were collected to calculate air density. Table 1 shows the sensors used, their ranges, and
accuracies. Appendix A lists the sensor scale and offset values used.
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Table 1: Meteorological sensor specifications
Sensor Type NRG
Part # Quantity Sensor Range Accuracy [Over Range]
Response
Threshold
Anemometer,
3-cup 40C 6
1 to 96 m/s
(2 to 214 mph)
± 0.1 m/s [5-25 m/s]
(± 0.2 mph) [11-55 mph]
0.78 m/s
(1.75 mph)
Wind Vane 200P 2 360° continuous ± 3.6° 1 m/s
(2.2 mph)
Temperature 110S 2 -40.0°C to 52.5°C
(-40°F to 127°F)
± 1.1°C
(± 2.0°F)
5-minute
time constant
Barometric
Pressure BP20 1
150 to 1150 hPa
(4.4 to 34.0 inHg)
± 15 hPa max. offset
(± 0.443 inHg) N/A
Relative
Humidity RH5 1
5 to 95%
relative humidity
± 5% RH
[5-95% at 25°C (77°F)] N/A
In order to measure the wind shear, the increase in wind speed with height, anemometers were
installed on the tower at three different levels: 102.4 m (336 ft), 81.1 m (266 ft), and 52.4 m (172 ft).
Hereafter, these will be referred to as the 102 m, 81 m, and 52 m levels. At each level, two
anemometers were installed on the west face of the tower, one on each side, in case one sensor were to
freeze or fail, or if the wind direction caused one sensor to be in the wind shadow of the tower. The
west face was chosen to minimize the effect of the tower on the measured wind speed, assuming a
predominantly westerly wind direction. To further minimize the effect of the tower on the
measurements, the anemometers were mounted on 2.9 m (9.4 ft) booms extending north and south
from the tower. At the 102 m level, where the face width is 1.2 m (4.0 ft) and the tower legs are 5 cm
(2 in) solid round bar, each boom was installed across the face, attached to each leg, such that the
sensors were approximately 1.6 m (5.3 ft) from the tower. At the 81 m level, where the face width is
1.7 m (5.5 ft) and the tower legs are 13 cm (5 in) pipe, each boom was mounted to only one leg, holding
the sensors approximately 2.7 m (8.8 ft) from the tower. Similarly, at the 52 m level, where the face
width is 3.5 m (11.5 ft) and the tower legs are 20 cm (8 in) pipe, each boom was mounted to one leg,
holding the sensors approximately 2.6 m (8.6 ft) from the tower. The ratios of boom extension to tower
face width at each level are 1.33, 1.60, and 0.75, for the 102 m, 81 m, and 52 m levels, respectively.
At the 102 m and 52 m levels, wind vanes were also installed on the north anemometer booms. Two
vanes were used for redundancy and were installed at different heights to see if there was any change in
direction with height. Temperature sensors were installed onto the south tower leg both at the 102 m
level and also at ground level onto an instrument cluster mounted at approximately 2 m (7 ft). Again,
two sensors were installed for redundancy and to measure the difference in temperature with height.
Also mounted to the ground level instrument cluster were the absolute pressure and relative humidity
sensors. Figure 2 shows the radio tower and the locations of the sensors. In the image, “S” indicates a
wind speed sensor or anemometer, “D” indicates a wind direction sensor or wind vane, “T” indicates a
temperature sensor, “H” indicates the relative humidity sensor, and “P” indicates the barometric
pressure sensor. Figure 3 shows the ground level instrument cluster mounted to the south tower leg.
4
Figure 2: WSIU radio tower with sensor mounting
locations, view from the east.
Figure 3: Ground level instrument cluster
attached to south tower leg at 2m.
Data Logger
All twelve sensors were wired to a data logger, shown in Figure 4, which was installed inside the radio
amplifier building at the base of the tower. To minimize interference from the radio equipment, the
sensor cables were shielded and bonded to the data logger and to earth ground. The 12-channel
Symphonie data logger from NRG Systems was used. The logger samples each of the channels every
two seconds and accumulates the samples over a fixed 10-minute interval. For each channel, the logger
calculates the minimum, maximum, average, and standard deviation of the samples over the 10-minute
interval and stamps the results with the time from the beginning of the interval. The results are stored
in the logger memory and written to a removable non-volatile memory card at the top of each hour.
Table 2 lists the resolution of the data logger for analog measurements and stored values.
Approximately every two weeks a visit was made to the site to collect the data. The new files, one for
each day, were copied to a laptop computer from the data logger’s removable memory card. The
Symphonie Data Retriever software, supplied with the data logger, was used to scale the raw data into
SI units and import it to a database for permanent storage and analysis. The scale and offset values that
were used are listed in Appendix A. The data collection period extended from 1:00 PM, November 19,
T
P
H
5
2007 to 11:50 PM, May 31, 2010, covering 2 years, 6 months, 12 days, and 11 hours. Hereafter, the
radio tower data will be referred to by its sensor height level, or collectively as the “site” data.
Table 2: Symphonie data logger resolution
Description Resolution
Analog measurement 0.1% of full scale (1024 counts)
Counter average stored value 0.1% of the value stored
Analog average stored value 0.1% of the value stored
Min / Max stored value 0.3% of the value stored
Standard deviation stored value 4.0% of the value stored
Figure 4: 12-channel data logger installed in radio amplifier building
KMDH Weather Data
A reference set of coincident surface weather data was collected from the Southern Illinois Airport
weather station, which is part of the Automated Surface Observing System (ASOS) network of the
National Weather Service (NWS) and the Federal Aviation Administration (FAA). Hereafter, this data set
will be referred to using the International Civil Aviation Organization (ICAO) code for Southern Illinois
Airport, KMDH. The automated weather station is located just east of the main airport runway at 37°
47’ 0.10” N and 89° 14' 44.00" W, 8.38 km (5.21 mi) north (354°) of the WSIU Radio Tower. The weather
station elevation is 122.2 m (401 ft) AMSL. Figure 5 shows the ASOS weather station with Southern
Illinois Airport in the background.
6
Figure 5: KMDH ASOS instrumentation array with Southern Illinois Airport in background
The KMDH data were obtained via the internet from the National Climatic Data Center in the form of
monthly files of 5-minute interval METAR (Meteorological Aviation Report) code.[3]
The METAR data
were imported to an Excel spreadsheet and decoded into weather data tables, which were then
imported to the same database as the site data. Appendix C of the 1998 ASOS User’s Guide was used to
decode the METAR and extract the relevant information.[4]
Table 3 lists the fields that were extracted,
the sensor types, ranges, reported resolutions, accuracies, and the frequency with which the ASOS
system sampled and averaged the data. For this data set, values were reported at 5-minute intervals.
The wind speed data were converted to units of meters per second (m/s) and the pressure data were
converted to units of hectopascals (hPa), equivalent to millibars (mbar). Unit conversions are listed in
Appendix A.
The KMDH data, reported at each 5-minute interval, cover the averaging period just elapsed as defined
in Table 3, while the radio tower data were time stamped at the beginning of each 10-minute averaging
interval. In order to match the time stamps of the radio tower and KMDH data covering the same time
period, the KMDH time stamps were adjusted to the beginning of the 10-minute clock interval in which
the data were averaged. For example, KMDH time stamps at 10:05 and 10:10 were both modified to
10:00. Both values of each KMDH parameter now sharing a common time stamp were averaged to
create a single value coincident with the radio tower data. Note that due to the different averaging
periods listed in Table 3, final KMDH wind values represent sampling over 40% of each 10-minute
period, while temperature and pressure values represent 100% and 20%, respectively.
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Table 3: ASOS sensor specifications and data characteristics [5]
Parameter Sensor Range Resolution Accuracy Sampling
Interval
Averaging
Period
Wind Speed Cup
Anemometer
3 to 125 KT
(1.5 to 64 m/s)
1 KT
(0.5 m/s)
Greater of:
± 2 KT (1 m/s)
or 5%
5 sec 2 min
Wind
Direction Wind Vane
0 to 360°
WS ≥ 3 KT 10°
± 5°, ≥ 5 KT
(≥ 2.6 m/s) 5 sec 2 min
Air
Temperature
Resistance
Temperature
Device (RTD)
-62°C to +54°C 1°C RMSE: 0.5°C
(-50°C – 50°C) 1 min 5 min
Dew Point
Temperature
Chilled
Mirror -62°C to +30°C 1°C
RMSE:
4.4°C (<0°C)
2.6°C (≥ 0°C)
1 min 5 min
Pressure
(Altimeter)
3 Redundant
Pressure
Cells
16.9 to 31.5
inHg
(572 to 1067
hPa)
0.01 inHg
(0.34 hPa)
± 0.02 inHg
(± 0.68 hPa)
1 min 1 min
Calculations
Wind Power Density
The power extracted from the wind by a horizontal-axis wind turbine is defined by the equation:
� = �����
� (1)
where � is the power in Watts, �� is a dimensionless power coefficient, � is the air density in kg/m3, is
the rotor swept area in m2, and is the wind speed normal to the rotor plane in m/s. The power
coefficient is the fraction of the total wind power that can be extracted by the turbine rotor and has a
theoretical limit of 59.3%, known as the Betz limit. Due to aerodynamic losses, the power coefficient of
a modern 3-blade turbine is typically less than 50%, but varies depending on the rotor design and with
the ratio of rotor tip speed to wind speed.[6]
While rotor efficiency and air density are important factors
in the generation of wind power, equation 1 illustrates that rotor diameter and especially wind speed
are the most important factors. Doubling the rotor diameter causes a four-fold increase in the rotor
area and thus the power generated, while doubling the wind speed causes an eight-fold increase in the
power.
Normalizing with respect to the rotor swept area and neglecting aerodynamic losses, a measure of the
raw power of the wind, independent of the choice of wind turbine, is the wind power density, which is
defined by the equation:
�� = �� �� �� (2)
8
where �� is the power per unit area, in W/m2 at height, ℎ . In order to characterize the power content
of the site wind resource, the wind power density was calculated using the wind speeds and air density
at each site measurement level and also at KMDH. Because the power density is proportional to the
cube of the wind speed, using a wind speed averaged over a long period of time would significantly
underestimate the wind power resource. Therefore, the wind power density was calculated for each 10-
minute interval in the data set.
Air Density
The air density was calculated at each 10-minute interval for inclusion in the wind power density
computation using the temperature, pressure, and humidity measurements with the following equation
derived from the ideal gas relationships for moist air: [7]
�� =�������� ���� ����(��)
���� (3)
where �� is the air density at height, ℎ, in kg/m3; �� is the atmospheric pressure at height, ℎ, in Pa; �
is molecular mass of water, 18.01528 u; �!" is the molecular mass of dry air, 28.9645 u; � #($!) is the
water vapor saturation pressure in Pa at the dew point temperature, $!; %!" is the gas constant for dry
air, 287.055 J/kgda·K; and & is the absolute air temperature in degrees Kelvin. From this equation it can
be inferred that high humidity (dew point) would reduce the air density, although, because the water
vapor pressure is relatively small compared to the total air pressure, the effect is small. It can also be
seen that increasing atmospheric pressure will raise the density and increasing temperature will lower
the density.
Air Pressure
The KMDH pressure data were reported as an altimeter setting for use in aviation, where the measured
pressure was corrected to sea level using the known elevation of the station and the properties of the
standard atmosphere.[8]
The standard atmosphere up to 11 km above sea level is defined by the
following assumptions:
• Fixed pressure at sea level, �' = 1013.25 hPa
• Fixed air temperature at sea level, &' = 288.15 K (15 °C)
• Constant temperature gradient with elevation, !�!( ≡ −+ = −6.5 x 10
-3 °C/m
• Constant gravitational acceleration, , = 9.80665 m/s2
• The atmosphere is a dry, ideal gas such that, � = �%!" &
• The atmosphere is in hydrostatic equilibrium such that, !�!( = −�,
Combining equations and integrating yields the following relationship between pressure and elevation,
-, in meters.
�(-) = �' ��.�/(�.�0���/�
(4)
9
Like the KMDH pressure data, the site pressure data were corrected to sea level based on the
measurement elevation. In order to calculate the wind power and air densities, the air pressure was
determined for each 10-minute interval using equation 4 at the elevations corresponding to each wind
speed measurement height, including KMDH. In addition, to demonstrate the effect of variations in air
density on a potential wind turbine, the normalized air density was calculated for each 10-minute
interval at a hub height of 100 m (328 ft) above the proposed wind turbine site:
1�� = ��� �'� (5)
where 1�� is a dimensionless air density factor, ��� is the air density calculated at the wind turbine hub
height, and �' is the density of the standard atmosphere at sea level, 1.225 kg/m3. Table 4 lists the
elevations that were used.
Table 4: Elevations used in air pressure calculations for wind power and air densities
Level KMDH Study Site Wind Turbine
Base Elevation
AMSL
122.2 m
(401 ft)
142.0 m
(466 ft)
137.2 m
(450 ft)
Wind Speed
Height AGL
10 m
(33 ft)
51.8 m
(170 ft)
81.1 m
(266 ft)
102.4 m
(336 ft)
100 m
(328 ft)
Wind Speed
Elevation AMSL
132.2 m
(434 ft)
193.8 m
(636 ft)
223.1 m
(732 ft)
244.4 m
(802 ft)
237.2 m
(778 ft)
Dew Point Temperature
The dew point is the temperature at which moist air of constant absolute humidity and constant
pressure reaches saturation. The dew point temperature is always less than or equal to the ambient, or
dry bulb, temperature and the dryer the air, the greater the difference. In order to compare the
humidity data of the site and KMDH sets, the site relative humidity data were converted to the
equivalent dew point temperature using the site temperature and pressure data and the methods
described in the 2005 ASHRAE Fundamentals Handbook.[9]
If any of the three parameters was missing at
a particular time stamp, no value was calculated for dew point temperature.
Wind Shear
At altitudes high above the ground the wind can be considered as geostrophic; i.e., governed solely by
large-scale pressure gradients and the Coriolis effect caused by the earth’s rotation. Closer to the
ground the wind is slowed by friction with the surface, which grows stronger with decreasing altitude,
causing a vertical wind speed profile known as wind shear. The region in which surface friction plays a
role is known as the boundary layer and its thickness is determined by a number of factors including the
geostrophic wind speed, thermal effects, and the surface roughness from obstructions such as hills,
trees, and buildings. Low wind shear, where there is little change in wind speed with height, can be
caused by rough terrain and by solar heating of the ground giving rise to warm buoyant convection cells.
These cells can also cause large turbulent eddies that result is sudden wind gusts. High wind shear,
where there is a marked increase in wind speed with height is associated with smooth terrain and with
cold nights when the air is stratified and does not mix vertically. High wind shear increases the average
10
power density across the rotor swept area, but can cause large asymmetrical stresses on the rotor
blades.[10]
One typical model used for wind shear is the power law model:
2324= �(3(4�
5 (6)
where 6 is the horizontal wind speed at height, -6, -� > -�, and 8 is the power law exponent that
characterizes the degree of wind shear. For turbine design, the 2005 IEC Standard 61400-1 assumes an
exponent value of 1/5th[11]
; and a value of 1/7th
is typically assumed when no measurements exist.[12]
However, as discussed above, 8 depends on the surface roughness and stability of the boundary layer
which can change seasonally and from day to night. Therefore three values of wind shear exponent
were calculated for each 10-minute interval for which simultaneous wind speed measurements at
different heights were available for the study site. Height intervals of 102m/52m, 102m/81m, and
81m/52m were used for -� -�⁄ in the following equation:
8 =:'04;�<3<4�:'04;�=3=4�
(7)
Turbulence Intensity
In addition to seasonal, diurnal, and synoptic variations in wind speed associated with weather fronts,
the wind is characterized by turbulent oscillations on short time scales of 10 minutes or less caused by
the friction and thermal effects described above. Highly turbulent wind can reduce power production
and induce severe stress onto the rotor. The degree of turbulence at the study site was characterized by
the turbulence intensity, calculated for each measurement height:
> = ?2 (8)
where @ is the standard deviation of the wind speed, and is the mean wind speed of the same 10-
minute interval. The 2005 IEC standard defines reference turbulence intensity,>ABC , for high, medium,
and low turbulence wind as 16%, 14%, and 12%, respectively, when measured at a mean wind speed of
15 m/s. [13]
Data Validation With the goal of verifying that the collected data were plausible and that sensors were functioning
correctly, the 10-minute site data underwent a validation process of screening and verification modeled
after the one described in the NREL Wind Resource Assessment Handbook.[14]
The screening consisted
of a series of tests based on range, relationships to contextual data, and rates of change. Each data
point that failed one or more of these tests was then verified by viewing a time-series plot of the field
containing the suspect data and other related fields. After viewing the data in context, a decision was
made to keep or reject the suspect data. Rejected data were kept but flagged with a number indicating
the reason for rejection and suspect data passing verification were flagged with a zero. The final data
11
set was compiled from the data passing screening and verification, and omitting the rejected data. No
substitutions were made. The KMDH data were not subjected to validation except by means of
comparison to the site data as described below.
Wind Speed
Data from the six site anemometers were subjected to screening for sensor range violations, but none
were found. Further, minimum and average values were checked if found above 20 m/s, and maximum
values were checked above 30 m/s, but all were associated with weather events that would explain the
high values. Icing events were found by reviewing data where the standard deviation was equal to zero
and the temperature near or below freezing. Frozen sensor data were flagged and excluded from the
final data set. Data loss due to icing events ranged from 1.0% at the 52 m level to 1.9% at the 102 m
level.
Zero standard deviation tests also identified many legitimate short periods of calm winds, but extended
periods indicated a failed sensor. The south anemometer at 102 m failed during a thunderstorm at
16:50 on 5/26/2010, presumably as the result of a lightning strike. The sensor was flagged as failed and
excluded from the data set from that point forward. The loss of that important sensor drove the
decision to end the data collection period five days later at 23:50 on 5/31/2010.
In addition to the high wind speed tests above, several screening tests were successful at identifying
storm fronts. These were: standard deviation greater than 5 m/s, a one-hour change in average wind
speed of 5 m/s or more, and the ratio of maximum to average wind speed greater than 2.5 while the
average was greater than 3 m/s. These tests were not effective in revealing problems with the sensors
but were useful for identifying strong storms with severe gusts.
During review of the data it was noticed that infrequently the wind speed would appear to suddenly and
briefly drop to zero and then return to normal, indicating a possible loss of signal from the sensor. After
some trial and error, a method that was particularly effective was devised to identify these incidents.
The test consisted of comparing the turbulence intensities of the redundant sensors at each level. If a
signal was lost and/or regained during a given 10-minute period, then its standard deviation would rise
at the same time its average would drop, causing a spike in the turbulence intensity. Comparison with
the redundant sensor ensured that the anomaly was related to the equipment and was not a true wind
event. The screening test found records where the absolute value of difference in turbulence
intensities between redundant sensors at a given level was greater than 0.4, and where the higher
average wind speed was greater than 3 m/s. Lower wind speed values were ignored for a number of
reasons: sensor accuracy degrades at low wind speeds; a small average speed causes the turbulence
intensity value to be very large; and most modern wind turbines do not produce power below 3 m/s. All
values failing the screening test were reviewed in context and, if confirmed, flagged for data loss and
removed from the final data set. This problem was seen most frequently with the north 102 m sensor,
where 0.8% of the data were lost, but rarely with the other 5 sensors whose combined data loss rate
was 16 per 100,000 records.
12
As previously discussed, two anemometers were used at each level and mounted on booms extending
from the tower in order to minimize as much as possible the effect of the tower on the wind speed
measurements. The wind shadow effect of the tower was analyzed by studying the difference between
the North and South anemometers at each level as a function of the wind direction. Figure 6 shows the
results of the wind shadowing analysis, where positive values indicate the North signal was greater and
vice-versa. The charts were created for each level by binning the direction data from the nearest wind
vane in increments of 10°, centered on the value shown, and then taking the minimum, maximum,
average, and standard deviation of the wind speed differences within each bin. Only wind speed data
passing the validation process described above, greater than 3 m/s, and from the period 11/19/2007 to
3/30/2008 were used. The area curve in the background of the charts shows the frequency of each
wind direction and lists the total data points used. The plots show, as expected, that in a southerly
wind, the North anemometer was in the wake of the tower, and vice-versa. Because the anemometers
were mounted on the west face of the tower and the third tower leg is to the east, the shadowing peaks
continue for winds east of due north and south. At the 102 m level, the second distinct peak at 150° is
caused by the FM antennae, which are mounted off the north side of the east tower leg as shown in
Figure 2. The reversals of average wind speed difference on either side of the tower wakes, for
example: at 60°, 140°, 210°, and 340° at the 81 m level, were likely caused by higher velocities at the
downwind sensor as the wind accelerated around the sides of the tower. However, the effect could also
have been caused in part by stalling of the wind ahead of the tower around the upwind sensor. At the
52 m level, where the ratio of boom extension to tower width is smallest, 0.75, the tower wakes are
widest, the variation in sensor difference is highest, and this acceleration/stall effect is most
pronounced. The 81 m level has the largest boom-to-tower ratio of 1.60, however, the 102 m level, with
a ratio of 1.33, has the least distortion most likely because the wind was not blocked by any tower
structure above.
In selecting which redundant anemometer signal to use in the final data set, the simplest method was
chosen. The maximum of the two 10-minute average values at each time stamp and each level was
used. The wind shadow analysis showed that, except for the direct tower wake, the sensor differences
at the 102 m and 81 m levels were small with little variation. The variation was greater at the 52 m
level; however, the upper level data is the most important for predicting wind speeds at the anticipated
wind turbine hub heights of 80-100 m. Nonetheless, the average sensor difference not due to the direct
tower wake was limited to 0.5 m/s. Where one sensor at a given level was unavailable or invalidated,
the second sensor was also excluded if the maximum signal from a lower level was taken from the other
side of the tower. This way, the backup signal was excluded if it was likely to have been in the wind
shadow of the tower.
13
Figure 6: Wind Shadowing of Radio Tower at 102 m, 81 m, and 52 m Levels Based on Wind Direction
0%
2%
4%
6%
8%
10%
12%
14%
-5
-4
-3
-2
-1
0
1
2
3
4
5
0
10
20
30
40
50
60
70
80
90
10
0
11
0
12
0
13
0
14
0
15
0
16
0
17
0
18
0
19
0
20
0
21
0
22
0
23
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0
25
0
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0
29
0
30
0
31
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32
0
33
0
34
0
35
0
36
0
North East South West North
Fre
qu
en
cy o
f W
ind
Sp
ee
d >
3 m
/s
Win
d S
pe
ed
Dif
fere
nce
(N
-S)
[m/s
]Wind Shadow of Tower at 102m
Frequency Maximum Average ± Standard Deviation Minimum
Total Records: 14,476
0%
2%
4%
6%
8%
10%
12%
14%
-5
-4
-3
-2
-1
0
1
2
3
4
5
0
10
20
30
40
50
60
70
80
90
10
0
11
0
12
0
13
0
14
0
15
0
16
0
17
0
18
0
19
0
20
0
21
0
22
0
23
0
24
0
25
0
26
0
27
0
28
0
29
0
30
0
31
0
32
0
33
0
34
0
35
0
36
0
North East South West North
Fre
qu
en
cy o
f W
ind
Sp
ee
d >
3 m
/s
Win
d S
pe
ed
Dif
fere
nce
(N
-S)
[m/s
]
Wind Shadow of Tower at 81m
Frequency Maximum Average ± Standard Deviation Minimum
Total Records: 14,677
0%
2%
4%
6%
8%
10%
12%
14%
-5
-4
-3
-2
-1
0
1
2
3
4
5
0
10
20
30
40
50
60
70
80
90
10
0
11
0
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0
13
0
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0
15
0
16
0
17
0
18
0
19
0
20
0
21
0
22
0
23
0
24
0
25
0
26
0
27
0
28
0
29
0
30
0
31
0
32
0
33
0
34
0
35
0
36
0
North East South West North
Fre
qu
en
cy o
f W
ind
Sp
ee
d >
3 m
/s
Win
d S
pe
ed
Dif
fere
nce
(N
-S)
[m/s
]
Wind Shadow of Tower at 52mFrequency Maximum Average ± Standard Deviation Minimum
Total Records: 12,929
14
Wind Direction
While an effort was made to align the wind vanes due north during their installation, some
misalignment was inevitable and efforts to measure the offsets after installation were complicated by
compass deflection caused by the tower. In order to align the sensor data, differences between
concurrent site and KMDH vane data, which were found to converge at higher wind speeds, were
analyzed as a function of the closest site wind speed measurement. It was assumed that the KMDH
wind vane was perfectly aligned. Lower wind speeds were excluded until the standard deviation of the
differences was minimized. The resulting average differences were used as the offsets for the two site
vanes. The offsets were verified by visual inspection. Table 5 lists the results of the comparison.
Table 5: Wind Vane Offset Determination
Vane Level Wind Speed Vane Offset Standard
Deviation
Number of
Data Points
Coefficient of
Determination
102 m ≥ 12 m/s +2° 7.1° 635 98%
52 m ≥ 10 m/s -9° 7.6° 774 96%
Data from both wind vanes was checked for range violations and none were found. As with the wind
speed data, icing conditions were identified by screening for zero standard deviation. High standard
deviations of 75° or more were reviewed and found to correspond to either flagging wind or abrupt
wind direction changes due to passage of storm fronts. Screening tests also showed that both wind
vanes permanently failed at 7:30 AM on 3/30/2008 during a thunderstorm, presumably due to a
lightning strike to the tower. The resistive sensing element in the vanes is susceptible to electrostatic
discharge. Due to the cost, difficulty in scheduling a broadcast outage for sensor replacement, and the
availability of the KMDH vane data, the decision was made not to replace the failed vanes. For the final
data set, the 102 m, 52 m, and KMDH vanes were used in descending priority. Figure 7 shows the
averages and standard deviations of the differences between the site and KMDH wind vanes as a
function of wind speed after the offsets were applied. The results show that on average the vanes are
within 10°, but that significant variation exists at low wind speeds, especially below about 5.5 m/s.
Figure 7: Variation between Site and KMDH Wind Vanes
-10
0
10
20
30
40
50
3 4 5 6 7 8 9 10 11 12 13 14 15
Dif
fere
nce
(D
eg
ree
s)
Wind Speed (m/s)
Variation between Site and KMDH Wind Vanes
Average
102 m - KMDH
Average
52 m - KMDH
Std. Deviation
102 m - KMDH
Std. Deviation
52 m - KMDH
15
However, the error in assuming equal wind direction at the site and at KMDH diminishes at higher wind
speeds of interest in wind power production. At 102 m, the standard deviation drops below 20° above
6 m/s and drops to about 7° at 12 m/s and above.
Temperature
The site temperature data from both the top sensor at 102m and the ground sensor at 2 m were
checked for excursions outside the range of -25°C to 40°C. No violations were found for the ground
sensor, however many extreme low temperature readings were found for the top sensor. As shown in
Figure 8, the top sensor showed wildly varying and unrealistic readings. The cause of the anomalies
could have been wire strain or sensor damage during installation, or more likely could have been
electro-magnetic interference (EMI) from the nearby FM radio antennae. The sensor used was an
integrated-circuit type that could be susceptible to EMI. Perhaps a more rugged sensor such as a
resistance temperature detector (RTD) would have been a better choice. Because of its erratic output,
the top temperature sensor was marked as a failed sensor and none of its data was used.
The ground temperature sensor data was screened for 1-hour changes in temperature greater than 5°C.
Eighty-five occurrences of these rapid temperature changes were found and reviewed, but all were
explained by storm fronts or similar legitimate weather events. Finally, the relationship between the
ground temperature and the KMDH temperature data was analyzed as part of the validation effort.
Figure 8 shows very good agreement between the site ground and KMDH sensors. Further analysis
showed that the difference between the hourly averages of the two sensors had an average of 0.28°C
and a standard deviation of 0.98°C, close to the combined standard error of the two sensors, 0.81°C.
Figure 9 shows a histogram of the sensor difference for 21,012 hours of data compared to a zero-mean
normal distribution with the same standard deviation. The plot illustrates the good agreement between
the sensors, but shows the distribution is skewed right, indicating that the site temperature was more
variable on the warm side than KMDH. The adjacent plot, showing the hourly variation of the
temperature difference, suggests that the cause could have been the thermal mass of the radio tower.
Figure 8: Comparison of Site Top, Site Ground, and KMDH Temperature Measurements
-60
-50
-40
-30
-20
-10
0
10
20
30
1-Nov 2-Nov 3-Nov 4-Nov 5-Nov 6-Nov 7-Nov 8-Nov 9-Nov 10-Nov 11-Nov 12-Nov 13-Nov 14-Nov 15-Nov
Tem
pe
ratu
re (
°C)
Comparison of Temperature Measurements - Nov. 2008
KMDH Site Ground Site Top
16
Figure 9: Histogram and Hourly Variation of Temperature Difference (Site Ground – KMDH)
The tower remained cool in the morning, but eventually solar heating of the tower raised the air
temperature slightly higher than at KMDH. Nonetheless, the effect was small and no site ground
temperature data was excluded as a result of the validation process. In reviewing the temperature
difference data, twenty-eight suspect records adjacent to periods of missing data were identified and
removed from the KMDH data. The KMDH temperature data was used as a backup source for
calculating air density where site records were missing. Where no temperature data were available, the
standard atmosphere default of 15°C was used in the air density calculation.
Humidity
The site relative humidity sensor was checked for excursions outside its range; 409 records were found
below 5% and 478 records were found above 95%. As discussed above, the data were converted to dew
point temperature in conjunction with the site ground temperature and pressure data. Figure 10 shows
a comparison of site and KMDH dew points over two weeks with the site temperature added as a
reference. The site dew point, significantly below the KMDH dew point on average, is both unrealistic
and erratic. On sunny days, November 1st
through 5th
, the site humidity drops consistently, but not on
overcast days, November 12th
through 14th
, indicating that the sensor may have been affected by solar
heating. The KMDH data follows a much more stable and realistic pattern on sunny days, where the
dew point rises as the morning sun evaporates moisture from the ground into the air and falls again as
the ground reabsorbs some moisture in the evening and night. Over the whole data set, the difference
between hourly averages of the two sensors had an average of -6.0°C and a standard deviation of 4.1°C,
both unacceptably large. All of the site humidity data were rejected and only the KMDH data were used
to calculate the air density. Where no dew point data were available, humidity was neglected in the air
density calculations.
0%
2%
4%
6%
8%
10%
12%
14%
-4 -3 -2 -1 0 1 2 3 4
Fre
qu
en
cy
Temperature Difference (°C)
Histogram of Temperature Difference
Normal
Data
Average: 0.28
St. Dev.: 0.98
N hours: 21012
-1
0
1
2
0 2 4 6 8 10 12 14 16 18 20 22Te
mp
era
ture
Dif
fere
nce
(°C
)
Hour
Temperature Difference by Hour
Average St. Deviation
17
Figure 10: Comparison of Site and KMDH Dew Point Temperature
Pressure
The site pressure data were screened for excursions beyond the range of 940 hPa to 1060 hPa and none
were found. Twenty-six occurrences were found where the average hourly pressure changed more than
3 hPa in one hour. These were all associated with legitimate weather events such as the passage of
storm fronts. Figure 11 shows a comparison of the site and KMDH pressure data. The site data were
offset low and while the trends tracked very well on overcast days, November 12 – 14, the diurnal
variation was much more pronounced on clear, sunny days, November 1 – 5. Over all the data, the
average difference of the hourly averages of the two sensors was found to be -3.6 hPa and the standard
deviation was found to be 2.4 hPa. Figure 12, which shows a histogram of these hourly average
differences and a reference normal distribution of the same standard deviation, illustrates the negative
offset of the site sensor below the KMDH sensor and the flat-peaked, nearly bimodal, distribution of the
Figure 11: Comparison of Site and KMDH Pressure Measurements
-10
-5
0
5
10
15
20
25
1-Nov 2-Nov 3-Nov 4-Nov 5-Nov 6-Nov 7-Nov 8-Nov 9-Nov 10-Nov 11-Nov 12-Nov 13-Nov 14-Nov 15-Nov
Tem
pe
ratu
re (
°C)
Comparison of Dew Point Temperature Measurements - Nov. 2008
Site Dew Point KMDH Dew Point Site Temperature
1,000
1,005
1,010
1,015
1,020
1,025
1,030
11/1 11/2 11/3 11/4 11/5 11/6 11/7 11/8 11/9 11/10 11/11 11/12 11/13 11/14 11/15
Se
a-L
ev
el P
ress
ure
(h
Pa
)
Comparison of Pressure Measurements - Nov. 2008
Site KMDH
18
Figure 12: Histogram and Hourly Variation of Pressure Difference (Site – KMDH)
data. The adjacent plot, showing the average monthly trend, suggests that the site pressure sensor is
not adequately temperature compensated. While the average offset is well within the uncalibrated
offset error of the site sensor, the dispersion is very large compared to the specified error of the KMDH
pressure sensors, 0.68 hPa. Because air pressure is such a critical measurement for aviation, the KMDH
pressure sensors have triple redundancy and much better accuracy than the inexpensive pressure
sensor used in this study. Therefore, it was decided to use the KMDH pressure data as the primary data
for the calculation of air density and the site pressure data as a backup. Where no pressure data were
available, standard sea-level pressure, 1013.25 hPa, was used in the air density calculations.
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4
Fre
qu
en
cy
Pressure Difference (hPa)
Histogram of Pressure Difference
Normal
Data
Average: -3.62
St. Dev.: 2.42
N hours: 20999
-8
-6
-4
-2
0
2
4
11
12 1 2 3 4 5 6 7 8 9
10
11
12 1 2 3 4 5 6 7 8 9
10
11
12 1 2 3 4 5
2007 2008 2009 2010
Pre
ssu
re D
iffe
ren
ce (
hP
a)
Pressure Difference by Month
Average St. Deviation
19
Final Data Set
After the validation process was completed, the site records that had passed the screening, verification,
and signal selection were compiled along with the KMDH data into a final data set. All calculations were
rerun at 10-minute intervals using the validated data and these results were added to the final data set
as well. The following tables summarize the number of records collected, validated, and selected for the
final data set. All listed data recovery rates are based on the maximum possible number of 10-minute
records in the data collection period.
Table 6: Summary of Data Collected
Collection Period: 13:00 19-Nov-2007 to 23:50 31-May-2010 Possible 10-Minute Records: 133,122
Data Source Parameter Records
Collected
Records
Missing
Raw Data
Recovery Rate
Site All 12 Channels 131,064 2,058 98.5%
KMDH
Wind Speed* 127,915 5,208 96.1%
Direction* 104,777 28,346 78.7%
Temperature 128,026 5,097 96.2%
Dew Point 127,710 5,413 95.9%
Pressure 127,901 5,222 96.1%
* For wind speeds < 3 KT (1.5 m/s) ASOS reports zero wind speed (calm) and direction is left null.
Table 7: Summary of Validation Results for Site Data
North 126480 (2527) 0 (1070) (193) - 794 127274 95.6%
South 126810 (2425) (763) (65) (218) - 783 127593 95.8%
North 128037 (1995) 0 (17) (14) - 1001 129038 96.9%
South 128290 (1964) 0 (10) (29) - 771 129061 96.9%
North 127870 (1391) 0 (8) 0 - 1795 129665 97.4%
South 128021 (1396) 0 (4) 0 - 1643 129664 97.4%
102m 15332 (1480) (112089) 0 - (769) 1394 16726 12.6%
52m 16081 (1418) (112089) 0 - (596) 880 16961 12.7%
102m 0 0 (131064) 0 - - 0 0 0.0%
2m 130979 0 0 0 - - 85 131064 98.5%
2m 0 0 (131064) 0 - - 0 0 0.0%
2m 131038 0 0 0 - - 26 131064 98.5%
Va
lid
ate
d D
ata
Re
cov
ery
Ra
te
Sp
ee
d <
Th
resh
old
Suspect Data Verification
102 m
81 m
52 m
Va
lid
ate
d
Da
ta
Fro
zen
Se
nso
r
Pa
sse
d
Re
vie
w
Pa
sse
d
Scr
ee
nin
g
Win
d S
pe
ed
Se
nso
r
Fa
ilu
re
Da
ta L
oss
Humidity
Temperature
Wind
Direction
Pressure
Sensor & Location
Win
d
Sh
ad
ow
20
Table 8: Summary of Signal Selections for the Final Data Set
Parameter/Calculation Signal/Condition Records
Used
Signal Data
Recovery Rate
Parameter
Data Recovery
Rate
Wind Speed
102 m North 51,779 38.9%
96.4% South 76,515 57.5%
81 m North 61,994 46.6%
97.1% South 67,212 50.5%
52 m North 56,489 42.4%
97.5% South 73,241 55.0%
10 m KMDH 127,915 96.1%
Wind Direction
Site 102 m 16,726 12.6%
81.0% Site 52 m 410 0.3%
KMDH 90,656 68.1%
Air Density
Temperature
Site 2 m 131,064 98.5%
99.9% KMDH 1,956 1.5%
Default 9 0.0%
Pressure
KMDH 127,901 96.1%
99.9% Site 2 m 5,128 3.9%
Default 0 0.0%
Dew Point KMDH 127,707 95.9%
99.9% Default 5,322 4.0%
Wind Shear
Exponent All Levels WS102 m > 3 m/s 106,936 80.3%
Turbulence
Intensity
102 m
WSh > 3 m/s
106,936 80.3%
81 m 103,762 77.9%
52 m 91,612 68.8%
21
Wind Study Results The following section displays the results of the wind study in tabular and graphical form. Table 9 lists
the summary statistics for the final data set including mean, standard deviation, and percentiles of the
measured and calculated parameters. Table 10 and Table 11 list the mean and standard deviation for
the same parameters on a monthly basis. For wind direction, the prevailing direction is listed with the
corresponding frequency and potential wind energy yield fraction for that month. The charts illustrate
the variability in the monthly and hourly means for all the parameters and also show the frequency
distributions to help characterize the overall variance.
Table 9: Summary Statistics for All Data
Parameter / Calculation
Me
an
Sta
nd
ard
De
via
tio
n
Percentiles
0% 1% 25% 50% 75% 99% 100%
Min Median Max
Wind Speed (m/s)
102 m
81 m
52 m
5.7
5.2
4.4
2.8
2.6
2.3
0.3
0.3
0.3
0.6
0.5
0.4
3.8
3.4
2.8
5.5
4.9
4.0
7.3
6.5
13.7
13.1
38.2
35.9
5.5 11.8 32.0
KMDH 3.2 2.5 0.0 0.0 1.5 2.8 4.6 10.5 30.4
Wind Power
Density (W/m2)
102m 197 319 0 0 32 99 231 1,493 32,136
81m 155 272 0 0 24 72 168 1,305 26,743
52m 102 202 0 0 14 40 100 978 19,005
KMDH 66 157 0 0 2 14 61 723 16,418
Air Density Factor 0.99 0.04 0.89 0.91 0.95 0.98 1.02 1.09 1.14
Temperature (°C) 2m 12.3 10.8 -20.0 -10.7 3.4 12.8 20.7 33.1 38.2
Dew Point (°C) KMDH 6.5 10.4 -24.0 -16.5 -2.0 7.0 16.0 23.0 27.0
Pressure (hPa) KMDH 1017 7 988 999 1013 1017 1022 1036 1045
Wind Shear
Exponent
(WS 102m > 3m/s)
102/81m 0.43 0.34 -1.35 -0.23 0.21 0.38 0.61 1.48 5.37
102/52m 0.44 0.33 -0.66 -0.03 0.22 0.37 0.59 1.54 4.18
81/52m 0.45 0.37 -1.15 -0.06 0.22 0.37 0.60 1.68 5.41
Turbulence
Intensity
(WS h > 3 m/s)
102m 0.13 0.06 0.01 0.03 0.08 0.13 0.17 0.29 0.94
81m 0.14 0.06 0.01 0.03 0.10 0.15 0.19 0.30 1.03
52m 0.18 0.07 0.02 0.04 0.14 0.18 0.22 0.34 1.13
22
Table 10: Monthly Mean ± Standard Deviation of Wind Speed and Wind Power Density,
Prevailing Wind Direction for Frequency and Wind Energy Yield
102 m 81 m 52 m KMDH 102 m 81 m 52 m KMDH Frequency Yield
Nov 6.1 ±2.6 5.7 ±2.5 4.8 ±2.3 3.4 ±2.8 220 ±236 178 ±204 117 ±150 78 ±115 210°(22%) 210°(40%)
Dec 5.5 ±2.9 5.1 ±2.7 4.3 ±2.4 3.0 ±2.3 195 ±292 156 ±244 102 ±175 54 ±117 210°(14%) 210°(27%)
Jan 7.1 ±3.2 6.6 ±3.1 5.6 ±2.8 4.6 ±3.1 368 ±504 308 ±439 198 ±303 154 ±254 210°(24%) 210°(35%)
Feb 6.7 ±2.9 6.3 ±2.8 5.3 ±2.6 4.1 ±2.5 300 ±368 249 ±321 165 ±235 96 ±159 180°(15%) 210°(26%)
Mar 6.6 ±2.7 6.1 ±2.6 5.2 ±2.4 4.4 ±2.6 275 ±362 224 ±329 147 ±254 116 ±216 180°(17%) 210°(34%)
Apr 6.4 ±2.6 5.9 ±2.4 5.0 ±2.3 3.9 ±2.6 234 ±287 188 ±251 128 ±197 93 ±167 180°(21%) 180°(30%)
May 6.0 ±3.0 5.5 ±2.7 4.6 ±2.4 3.3 ±2.6 228 ±370 177 ±304 112 ±225 69 ±156 180°(15%) 180°(21%)
Jun 5.8 ±2.9 5.4 ±2.7 4.5 ±2.4 3.6 ±2.5 196 ±240 156 ±204 99 ±149 69 ±114 210°(33%) 210°(52%)
Jul 4.3 ±2.1 3.9 ±1.9 3.1 ±1.5 2.0 ±1.8 81 ±125 59 ±92 31 ±58 18 ±48 210°(18%) 210°(29%)
Aug 3.9 ±1.9 3.5 ±1.6 2.8 ±1.3 1.5 ±1.5 61 ±97 42 ±69 22 ±38 10 ±23 0°(17%) 30°(17%)
Sep 4.7 ±2.4 4.1 ±2.1 3.3 ±1.9 1.9 ±2.1 112 ±264 78 ±212 46 ±150 27 ±108 330°(19%) 150°(16%)
Oct 5.4 ±2.2 4.7 ±1.9 3.8 ±1.7 2.2 ±2.0 142 ±139 95 ±97 53 ±61 24 ±42 180°(15%) 210°(17%)
Nov 5.8 ±2.4 5.1 ±2.1 4.3 ±1.9 2.9 ±2.1 178 ±183 127 ±148 79 ±111 42 ±68 330°(16%) 330°(18%)
Dec 7.1 ±3.3 6.4 ±3.0 5.5 ±2.7 4.7 ±2.9 359 ±424 271 ±342 181 ±247 143 ±220 180°(24%) 180°(45%)
Jan 6.0 ±2.6 5.5 ±2.5 4.8 ±2.2 3.9 ±2.3 219 ±286 174 ±245 118 ±183 77 ±116 210°(19%) 210°(36%)
Feb 7.2 ±3.1 6.6 ±3.0 5.8 ±2.8 4.5 ±2.8 367 ±501 300 ±435 210 ±334 130 ±209 180°(20%) 210°(28%)
Mar 6.8 ±3.3 6.3 ±3.2 5.5 ±3.0 4.5 ±3.1 338 ±492 288 ±453 204 ±354 145 ±263 180°(17%) 210°(47%)
Apr 6.8 ±2.8 6.2 ±2.7 5.4 ±2.6 4.1 ±2.7 282 ±344 232 ±307 163 ±237 101 ±158 300°(16%) 210°(31%)
May 4.9 ±2.5 4.4 ±2.3 3.7 ±2.0 2.5 ±2.2 137 ±597 105 ±489 65 ±344 44 ±382 180°(17%) 180°(22%)
Jun 4.8 ±2.1 4.3 ±1.9 3.5 ±1.7 2.4 ±1.9 102 ±119 75 ±89 42 ±57 24 ±45 210°(17%) 210°(22%)
Jul 4.3 ±2.1 3.8 ±1.8 3.2 ±1.5 2.1 ±1.7 80 ±108 57 ±80 33 ±52 19 ±41 330°(15%) 210°(19%)
Aug 4.8 ±2.1 4.2 ±1.9 3.4 ±1.7 2.2 ±2.0 100 ±116 70 ±87 41 ±59 24 ±51 180°(20%) 210°(20%)
Sep 4.3 ±2.1 3.7 ±2.0 3.1 ±1.7 1.8 ±1.8 82 ±131 59 ±103 36 ±74 17 ±42 60°(29%) 60°(24%)
Oct 5.8 ±2.3 5.2 ±2.1 4.3 ±1.9 3.1 ±2.0 172 ±206 126 ±161 80 ±118 43 ±82 180°(15%) 180°(24%)
Nov 5.6 ±2.4 5.1 ±2.2 4.2 ±2.0 2.7 ±2.1 163 ±186 124 ±152 78 ±114 36 ±62 180°(21%) 210°(37%)
Dec 6.0 ±2.6 5.5 ±2.5 4.9 ±2.4 3.6 ±2.4 221 ±320 180 ±280 134 ±233 77 ±142 150°(13%) 210°(20%)
Jan 5.5 ±2.2 5.1 ±2.1 4.4 ±1.9 3.3 ±2.0 159 ±183 125 ±146 87 ±110 49 ±68 330°(21%) 300°(22%)
Feb 4.7 ±2.2 4.4 ±2.0 3.8 ±1.9 2.8 ±2.0 111 ±141 89 ±114 64 ±88 37 ±62 300°(26%) 300°(39%)
Mar 6.0 ±2.4 5.4 ±2.2 4.6 ±2.0 3.4 ±2.3 190 ±233 147 ±199 96 ±152 65 ±138 330°(21%) 210°(20%)
Apr 6.6 ±3.1 6.2 ±2.9 5.3 ±2.8 4.0 ±3.2 288 ±379 240 ±338 167 ±261 117 ±204 180°(27%) 210°(43%)
May 5.4 ±2.6 4.9 ±2.4 4.1 ±2.2 2.9 ±2.3 162 ±236 125 ±196 82 ±144 45 ±92 180°(14%) 210°(28%)
20
09
20
10
Wind Power Density (W/m2) Prevailing Wind (30°)
MonthWind Speed (m/s)
20
07
20
08
23
Table 11: Monthly Mean ± Standard Deviation of Air Density, Pressure, Temperature, Dew Point,
Wind Shear Exponent, and Turbulence Intensity
Density Pressure Temp. Dew Pt.
(%) (hPa) (°C) (°C) 102/81 m 102/52 m 81/52 m 102 m 81 m 52 m
Nov 101.6 ±2.9 1021 ±7 5.9 ±6.6 1.8 ±8.9 33 ±32 39 ±26 43 ±27 13 ±6 14 ±6 17 ±6
Dec 102.2 ±2.2 1019 ±6 3.6 ±4.8 -0.5 ±4.8 33 ±29 41 ±25 45 ±27 13 ±6 14 ±6 17 ±7
Jan 103.6 ±3.9 1022 ±10 0.8 ±8.0 -5.4 ±9.1 34 ±24 40 ±22 42 ±23 13 ±5 15 ±6 18 ±6
Feb 102.8 ±3.0 1017 ±7 1.6 ±6.6 -3.0 ±6.9 32 ±29 39 ±28 43 ±32 14 ±6 16 ±6 19 ±6
Mar 100.4 ±2.8 1018 ±7 7.9 ±6.6 2.1 ±5.6 36 ±27 39 ±25 41 ±27 14 ±6 16 ±6 19 ±6
Apr 98.3 ±2.6 1016 ±6 13.2 ±6.6 6.1 ±5.2 41 ±29 42 ±29 42 ±33 14 ±6 15 ±6 18 ±6
May 96.1 ±2.0 1012 ±6 18.0 ±5.5 11.6 ±4.3 39 ±31 43 ±29 46 ±32 13 ±7 14 ±7 18 ±7
Jun 93.9 ±1.8 1014 ±4 25.2 ±5.0 17.3 ±3.5 38 ±32 44 ±33 46 ±38 13 ±6 14 ±7 18 ±7
Jul 93.6 ±1.7 1016 ±3 25.8 ±5.1 19.3 ±2.8 44 ±41 54 ±46 60 ±54 12 ±7 13 ±7 16 ±8
Aug 94.2 ±1.8 1015 ±3 24.3 ±5.2 18.2 ±3.1 58 ±49 64 ±53 67 ±61 12 ±7 13 ±7 18 ±8
Sep 95.7 ±2.2 1018 ±5 21.0 ±5.9 15.6 ±4.1 62 ±45 60 ±45 60 ±53 12 ±7 13 ±7 17 ±8
Oct 98.4 ±2.6 1022 ±5 14.6 ±6.8 7.4 ±6.4 59 ±40 56 ±42 54 ±49 11 ±7 12 ±7 15 ±8
Nov 101.0 ±2.8 1019 ±9 6.9 ±6.6 0.1 ±6.1 54 ±35 47 ±30 44 ±32 12 ±6 14 ±6 17 ±7
Dec 103.1 ±3.4 1021 ±8 1.7 ±7.1 -3.1 ±7.4 44 ±26 38 ±21 35 ±22 14 ±5 16 ±5 20 ±5
Jan 104.1 ±3.3 1020 ±9 -1.0 ±6.7 -6.5 ±6.8 36 ±27 35 ±22 35 ±23 14 ±5 16 ±6 18 ±6
Feb 102.1 ±3.4 1021 ±8 4.4 ±7.4 -2.4 ±7.6 38 ±28 37 ±23 36 ±23 14 ±5 16 ±6 19 ±6
Mar 99.6 ±3.5 1019 ±9 10.6 ±7.8 2.6 ±7.3 37 ±30 38 ±27 38 ±27 14 ±6 16 ±6 19 ±6
Apr 97.9 ±2.5 1015 ±7 14.0 ±7.0 7.0 ±6.4 39 ±29 38 ±25 37 ±26 14 ±6 16 ±6 19 ±6
May 96.1 ±2.1 1016 ±6 19.1 ±5.4 13.8 ±4.9 47 ±37 49 ±35 49 ±38 13 ±7 14 ±7 18 ±8
Jun 93.4 ±2.0 1012 ±3 25.6 ±5.7 19.5 ±3.9 47 ±35 50 ±35 52 ±40 13 ±7 14 ±7 17 ±7
Jul 94.3 ±1.5 1015 ±3 23.7 ±4.3 18.4 ±2.8 50 ±38 49 ±34 49 ±36 12 ±7 14 ±7 17 ±7
Aug 94.5 ±1.8 1017 ±3 23.6 ±4.9 18.7 ±3.4 55 ±41 54 ±42 53 ±48 11 ±7 13 ±7 16 ±7
Sep 95.4 ±1.8 1017 ±3 21.2 ±5.0 15.9 ±3.9 59 ±41 57 ±44 56 ±50 13 ±6 14 ±7 19 ±7
Oct 98.5 ±2.0 1016 ±6 12.5 ±4.5 7.5 ±4.2 50 ±30 46 ±31 44 ±36 13 ±6 15 ±6 18 ±7
Nov 99.5 ±2.0 1019 ±5 10.8 ±5.3 4.5 ±4.5 47 ±36 48 ±35 49 ±38 11 ±6 12 ±6 16 ±6
Dec 102.9 ±2.4 1018 ±9 1.5 ±4.8 -3.3 ±5.7 41 ±31 37 ±27 35 ±29 13 ±5 14 ±5 17 ±6
Jan 104.6 ±3.9 1021 ±10 -1.8 ±7.7 -6.1 ±8.3 36 ±28 35 ±27 34 ±30 13 ±5 14 ±5 17 ±5
Feb 103.5 ±2.0 1018 ±5 -0.1 ±4.8 -5.4 ±4.0 35 ±30 35 ±30 34 ±34 13 ±5 14 ±6 17 ±6
Mar 99.7 ±2.6 1014 ±8 8.9 ±6.0 2.9 ±5.0 43 ±31 42 ±30 42 ±33 12 ±6 14 ±6 17 ±6
Apr 96.7 ±2.3 1014 ±8 17.2 ±6.2 8.8 ±4.8 35 ±35 40 ±35 43 ±40 13 ±7 14 ±7 17 ±7
May 95.4 ±2.2 1015 ±5 20.6 ±5.7 15.2 ±4.8 41 ±35 43 ±34 45 ±38 14 ±7 15 ±7 18 ±7
20
08
20
09
20
10
Wind Shear Exponent (x100) Turbulence Intensity (%)Month
20
07
24
Wind Speed
Figure 13: Monthly Mean Wind Speed
Figure 14: Distribution and Hourly Mean of Wind Speed for All Data
0
1
2
3
4
5
6
7
8N
ov
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
2007 2008 2009 2010
Win
d S
pe
ed
(m/s
)
Monthly Mean Wind Speed for All Data
102m 81m 52m KMDH
0%
2%
4%
6%
8%
10%
12%
0 3 6 9 12 15
Da
ta F
req
ue
ncy
pe
r b
in
Wind Speed (m/s), bins = 0.5
Distribution of Wind Speed for All Data
102m
81m
52m
KMDH
0
1
2
3
4
5
6
7
0 3 6 9 12 15 18 21 24
Win
d S
pe
ed
(m/s
)
Hour (CST)
Hourly Mean Wind Speed for All Data
102m 81m 52m KMDH
25
Wind Direction
Figure 15: Monthly Energy Yield Fraction by Wind Direction
Figure 16: Wind Roses for Frequency, 102 m Energy Yield, and 102 m Wind Speed Percentiles
0°
30°
60°
90°
120°
150°
180°
210°
240°
270°
300°
330°
Wind Rose for All Data
Frequency
102m Yield
Wind Direction, bins = 10°
15%
10%
5%
0°
30°
60°
90°
120°
150°
180°
210°
240°
270°
300°
330°
102 m Wind Speed Percentile Rose50%
75%
90%
95%
99%
Wind Direction, bins = 10°
20 m/s
15 m/s
26
Wind Power Density
Figure 17: Monthly Mean Wind Power Density
Figure 18: Distribution and Hourly Mean of Wind Power Density
0
50
100
150
200
250
300
350
400N
ov
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
2007 2008 2009 2010
Win
d P
ow
er
De
nsi
ty (W
/m2)
Monthly Mean Wind Power Density
102m 81m 52m KMDH
0%
1%
2%
3%
4%
5%
6%
7%
8%
10 100 1000
Da
ta F
req
ue
ncy
pe
r b
in
Wind Power Density (W/m2), bins = 10 0.1
Distribution of Wind Power for All Data
102m
81m
52m
KMDH
0
50
100
150
200
250
0 3 6 9 12 15 18 21 24
Win
d P
ow
er
De
nsi
ty (W
/m2)
Hour (CST)
Hourly Mean Wind Power for All Data
102m 81m 52m KMDH
27
Air Density Factor
Figure 19: Monthly Mean Air Density Factor
Figure 20: Distribution and Hourly Mean of Air Density Factor
-10
-5
0
5
10
15
20
25
30
0.925
0.950
0.975
1.000
1.025
1.050
No
v
De
cJa
n
Feb
Ma
r
Ap
r
Ma
yJu
n
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
vD
ec
Jan
Feb
Ma
r
Ap
rM
ay
2007 2008 2009 2010
Te
mp
era
ture
, De
w P
oin
t (°
C)
Air
De
nsi
ty F
act
or
(-)
Pre
ssu
re (
hP
a x
10
-3)
Monthly Mean Air Density Factor
Air Density Factor Sea-Level Pressure Temperature Dew Point
0.85 0.90 0.95 1.00 1.05 1.10 1.15
0%
2%
4%
6%
8%
10%
12%
14%
-20 -10 0 10 20 30 40
Temperature, Dew Point (°C), bins = 2
Da
ta F
req
ue
ncy
pe
r b
in
Air Density Factor, bins = 0.01
Pressure (hPax10-3), bins = 0.002
Air Density Factor Distrbutions
Temperature Dew Point
Air Density Factor Pressure
0
4
8
12
16
20
0.97
0.98
0.99
1.00
1.01
1.02
0 3 6 9 12 15 18 21 24
Te
mp
era
ture
, De
w P
oin
t (°
C)
Air
De
nsi
ty F
act
or
(-)
Pre
ssu
re (h
Pa
x 1
0-3
)
Hour (CST)
Hourly Mean Air Density Factor
Air Density Factor Pressure
Temperature Dew Point
28
Wind Shear Exponent
Figure 21: Monthly Mean Wind Shear Exponents
Figure 22: Distribution and Hourly Mean of Wind Shear Exponents
0.3
0.4
0.5
0.6
0.7N
ov
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
2007 2008 2009 2010
Win
d S
he
ar
Exp
on
en
t
Monthly Mean Wind Shear
102/81m 102/52m 81/52m
0%
5%
10%
15%
20%
-0.5 0.0 0.5 1.0 1.5 2.0
Da
ta F
req
ue
ncy
pe
r b
in
Wind Shear Exponent, bins = 0.1
Distribution of Wind Shear for All Data
102/81m
102/52m
81/52m
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 3 6 9 12 15 18 21 24
Win
d S
he
ar
Exp
on
en
t
Hour (CST)
Hourly Mean Wind Shear for All Data
102/81m
102/52m
81/52m
29
Turbulence Intensity
Figure 23: Monthly Mean Turbulence Intensity
Figure 24: Distribution and Hourly Mean of Turbulence Intensity
0.10
0.12
0.14
0.16
0.18
0.20N
ov
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Ma
r
Ap
r
Ma
y
2007 2008 2009 2010
Tu
rbu
len
ce In
ten
sity
Monthly Mean Turbulence Intensity
102m 81m 52m
0%
5%
10%
15%
20%
0.0 0.1 0.2 0.3 0.4 0.5
Da
ta F
req
ue
ncy
pe
r b
in
Turbulence Intensity, bins = 0.025
Distribution of Turbulence for All Data
102m
81m
52m
0.00
0.05
0.10
0.15
0.20
0.25
0 3 6 9 12 15 18 21 24
Tu
rbu
len
ce In
ten
sity
Hour (CST)
Hourly Mean Turbulence for All Data
102m
81m
52m
30
Analysis
KMDH Historical Reference Data
The results presented in the previous section cover two and half years, characterizing the wind resource
variability over diurnal and seasonal time scales. However, in order to use the study results to project
the future potential for wind energy production, assumptions must be made about the long-term annual
variability of the wind resource. In order to characterize the historical variability of the local wind
resource, an extended set of hourly data from KMDH was obtained from the National Climatic Data
Center.[15]
The dataset is comprised of hourly observations from the automated ASOS system since it
was installed in 1998 and manual observations prior to that back to 1973. Analysis of the dataset
inventory showed that prior to 1998, nighttime records were infrequent if not rare, but that the daytime
hours between 7 AM and 7 PM were fairly consistently represented throughout the dataset. Figure 26
illustrates the increase in nighttime reporting after the advent of ASOS in 1998. Because the KMDH
Figure 25: Annual Hours Observed at KMDH, 1973 – 2009
wind speed data were shown to have significant dependence on time of day (see Figure 14), the missing
nighttime hours would have caused an artificial negative trend in the annual mean wind speed
calculation. Therefore, only the daytime hours, 7-18, were considered in the analysis of the historical
wind resource. Figure 26 shows the annual mean and standard deviation of the daytime wind speeds
for KMDH. The annual mean has been nearly constant since the ASOS system began, with more
variability in the previous years, although the standard deviation has been relatively constant
throughout. Assuming there is no long-term trend in the annual means, the P-scores for significance of
the apparent weak downward trends are 99% for the ASOS data and 92% for all the data. These indicate
the probability that the apparent trends could be generated by random chance. Based on this analysis it
was assumed that there is no long-term trend in the KMDH wind speed. The reduced variability since
1998 could indicate that the manual data collection methods could have biased the annual means. The
constant standard deviation over the 37 year period is another indicator of a stable wind resource.
0
4,380
8,760
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
Ho
urs
pe
r Y
ea
r
Hours per Year with at Least One Observation
All Hours Daytime Hours (7-18)
31
Figure 26: KMDH Annual Mean Wind Speed, Daytime Hours 7-18, 1973 – 2009
Wind Speed Distribution for Estimating Annual Energy Production
Based on the finding of no significant trend in the long-term wind resource at KMDH, it was assumed
that the same was true of the study site. Further, it was assumed that the 2.5 years of 10-minute
interval data collected at the site was sufficient to represent the natural variability of the site wind
resource across all time scales relevant to a wind turbine installation. Therefore, wind speed frequency
distributions were developed from the full validated site dataset for use in estimating the average long-
term annual energy production of a candidate wind turbine.
The site wind speed data were binned in increments of 0.5 m/s, counted, and normalized by the total
number of records to create frequency distributions for each height level. These were shown in Figure
14 for all the data collected. Because significant seasonal variability was found in the wind speed at all
levels, as shown in Figure 13, and the collection period covered 2.5 years, using the raw distributions for
all data would bias the annual energy production estimates. Therefore, to remove the bias, wind speed
distributions were developed for each month where the data for a given month across multiple years
was combined to create 12 representative monthly distributions. Each bin count was then normalized
by the total records used for that month to create 12 frequency distributions for each level. Figure 27
shows these distributions for the 102 m level as a contour plot where each contour represents 1% of the
total time for that month where the wind speed was within the bin indicated. Bins were centered on
the value shown. Finally, the monthly profiles were averaged within each bin to create an average
annual frequency distribution for each level. The frequencies used for each bin were weighted by the
number of days in each month with February given a weight of 28.25 days to account for leap years.
The resulting average annual frequency distributions for each level are listed in Table 12 and shown in
Figure 28 along with a representative wind turbine power curve and resulting yield curve for the 102 m
profile. The figure illustrates that the annual energy yield depends on the overlap between the wind
speed profile and the turbine power curve. More overlap results in a larger yield curve, more hours of
production at a given turbine power output. The sum of the yield frequencies across all wind speed bins
gives the expected annual normalized energy output, or capacity factor, of the wind turbine when
trend = -0.012 m/s/yr trend = -0.011 m/s/yr
0
1
2
3
4
5
1970 1975 1980 1985 1990 1995 2000 2005 2010
Win
d S
pe
ed
(m
/s)
Annual Mean Wind Speed for KMDH (Daytime Hours 7-18)
Mean, All Data Mean, ASOS Data St. Deviation
Ho: There is no long-term trend.
P(All Data) = 92%, P(ASOS Data) = 99%
32
deployed with a hub height equal to the wind speed measurement level shown. The figure illustrates
that capacity factor will be increased both by installing the turbine at a higher hub height and by
choosing a turbine with lower cut-in (start-up) and rated (full-power) wind speeds.
Figure 27: Average Monthly Distribution of Wind Speed, 102 m Level
Figure 28: Average Annual Distribution of Wind Speeds, Each Level, with Representative Wind
Turbine Power Curve and 102 m Yield Curve
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Tu
rbin
e P
ow
er
An
nu
al W
ind
Sp
ee
d F
req
ue
ncy
Measured Wind Speed (m/s), bins = 0.5
Average Annual Wind Speed Distributions
52m Wind
81m Wind
102m Wind
Turbine Power
102m Yield
33
Table 12: Average Annual Wind Speed Frequency Distributions
Wind Speed (m/s) Average Annual Frequency of Occurrence
102 m Level 81m Level 52m Level
0.0 102 m Wind 81m Wind 52m Wind
0.5 1.64% 1.91% 3.13%
1.0 1.91% 2.24% 3.26%
1.5 2.79% 3.22% 4.42%
2.0 3.65% 4.30% 6.43%
2.5 4.55% 5.51% 8.43%
3.0 5.47% 6.93% 10.08%
3.5 6.30% 7.78% 10.83%
4.0 7.06% 8.72% 10.76%
4.5 7.62% 8.76% 9.37%
5.0 7.67% 8.61% 7.54%
5.5 7.70% 8.15% 5.69%
6.0 7.23% 7.25% 4.21%
6.5 6.75% 5.88% 3.32%
7.0 6.17% 4.65% 2.53%
7.5 5.15% 3.39% 2.13%
8.0 3.99% 2.59% 1.71%
8.5 3.05% 2.07% 1.33%
9.0 2.44% 1.63% 1.05%
9.5 1.82% 1.36% 0.85%
10.0 1.47% 1.04% 0.68%
10.5 1.20% 0.88% 0.56%
11.0 0.94% 0.68% 0.41%
11.5 0.77% 0.59% 0.37%
12.0 0.64% 0.46% 0.27%
12.5 0.52% 0.37% 0.18%
13.0 0.37% 0.29% 0.14%
13.5 0.31% 0.22% 0.11%
14.0 0.24% 0.14% 0.06%
14.5 0.16% 0.12% 0.04%
15.0 0.12% 0.09% 0.03%
15.5 0.09% 0.05% 0.02%
16.0 0.06% 0.04% 0.01%
16.5 0.04% 0.03% 0.01%
17.0 0.03% 0.02% 0.00%
17.5 0.02% 0.01% 0.00%
18.0 0.02% 0.01% 0.00%
18.5 0.01% 0.01% 0.00%
19.0 0.01% 0.00% 0.00%
19.5 0.01% 0.00% 0.00%
≥ 20.0 0.01% 0.00% 0.00%
Total 100.00% 100.00% 100.00%
34
Conclusion A detailed study of the wind resource on the SIUC campus was conducted in order to help determine the
feasibility of constructing a wind turbine for electric power generation. The study was conducted by
placing meteorological instrumentation on the WSIU FM radio tower on the west side of campus.
Redundant anemometers were placed at three levels on the tower: 102 m, 81 m, and 52 m above
ground level. Wind direction, temperature, pressure, and humidity were also measured. Ten-minute
interval data were collected over a period lasting just over 2.5 years from 13:00 19-Nov-2007 through
23:50 31-May-2010. Concurrent, five-minute interval reference data were collected from the National
Weather Service station at the Southern Illinois Airport (KMDH), located 8.38 km (5.21 mi) north of the
study site. Thirty-seven years, 1973 – 2009, of hourly data from the same airport weather station were
also collected and analyzed, validating the assumption that the long-term wind resource is stable and
without significant trends.
The site-collected data were subjected to an extensive validation process, including screening for sensor
errors, tower-induced effects, extreme weather events, and correlation to the KMDH reference data set.
The study suffered the complete loss of the temperature sensor placed at 102 m, although the ground-
level temperature sensor provided good data for the duration of the study. The humidity and pressure
sensors were found to have offset and scale errors that justified using the KMDH data instead. The wind
vanes were destroyed by lightning after 4.5 months, but enough direction data was collected to
determine tower wind shadowing effects and to develop a good correlation with the nearby airport such
that the sensors did not need to be replaced. The anemometers performed well, but some data was lost
due to icing conditions, infrequent problems with signal loss, and a data logger memory problem that
lasted two weeks in August 2008. The redundant signals at each level were used to minimize wind
shadowing effects from the radio tower. In all, the validated data recovery rate for wind speed was
approximately 97%.
The results showed significant seasonal and diurnal variability in the wind speed as well as a significant
improvement in the wind power density with measurement height. The monthly mean wind speed at
the 102 m level ranged from 3.9 m/s in Aug. 2008 to 7.2 m/s in Feb. 2009 with an overall mean of 5.7
m/s and a standard deviation of 2.8 m/s. On average, the lowest wind speeds were found to occur
around dawn and the highest around 9:00 PM for the upper two levels. Wind shear was found to have a
very significant dependence on time of day, with the wind shear exponent ranging from 0.2 during the
day to 0.6 at night. Turbulence intensity was found to decrease with increasing height, with overall
means ranging from 18%, to 14%, to 13% at the 52 m, 81m, and 102 m levels, respectively. However,
turbulence intensity was found to depend primarily on solar input, ranging up to 18% at noon and down
to 10% at night at the 102 m level. The monthly mean air density factor at the proposed wind turbine
site, dependent on the inverse of temperature, was found to range from 93% in Jun. 2009 to 105% in
Jan. 2010 with an overall mean of 99% and standard deviation of 4%. The overall mean wind power
density increased with height from 102, to 155, to 197 W/m2 at the 52 m, 81m, and 102 m levels,
respectively.
35
In order to determine the expected annual energy production for a wind turbine, average annual wind
speed distributions were created for each measurement level. For each level, data from common
months were pooled together and, to remove any seasonal bias, the months were combined into a
time-weighted average annual distribution. Since no significant trend was found in the KMDH historical
wind speed data, it was assumed that the average annual distributions are sufficient for estimating the
expected annual energy production over the 20-30 year life of a wind turbine. The annual energy
production is highly dependent on the compatibility of the wind turbine power curve and the hub-height
wind profile. While the selection of the best wind turbine for the site is beyond the scope of this report,
the study findings do suggest some criteria to guide that effort: a hub height of 100 m above ground
level is recommended to access the higher wind speeds and higher power densities; capacity factor will
be increased by a low cut-in speed with a steep rise in the power curve to the rated output at a low wind
speed; the ideal turbine would have a rotor diameter as large as possible, but high nighttime wind shear
could create significant rotor stress loads that would warrant a smaller rotor for the sake of reliability.
Suggestions for Future Research For future studies it is recommended that limited resources be spent to measure wind speed with
redundant high-quality sensors at the proposed hub height as was done in this study. Care should be
taken to minimize the effects of wind shadowing from the met tower; while dual sensors at each level
did largely accomplish that goal, three sensors would have been better due to the triangular shape of
the tower. Resistance-based wind vane sensors are highly susceptible to electrostatic discharge (ESD).
More care could have been taken to shield these sensors from ESD damage. If other cost-effective non-
resistive sensors are available, that would be preferred. Of the environmental sensors used to calculate
air density, only temperature is important to measure at the proposed development site. I believe it is
important to measure both hub height and ground level temperatures because there can be large
differences for high hub heights. Pressure is an important measurement, but is readily available from
local airports where the instrumentation is of very high quality as it is used for altimeter measurements.
Humidity does play a minor role in reducing air density, but again the chilled mirror dew point sensor
technology used by the ASOS sites at airports is much more accurate than the low cost relative humidity
sensors available for temporary met tower installations. In using local airport data for pressure and
humidity it is assumed that there are insignificant differences between the airport and the development
site.
Further useful analyses, not presented in this report, but based on the data collected could include the
following:
• Statistical analyses:
o Parameter estimation of Weibull distributions. These distributions are useful for
approximating the measured wind speed distributions using only two parameters that
determine the shape and scale of the distribution. The parameters allow for quick
comparisons to other wind profiles and turbine power curves. A goodness-of-fit
36
measure such as root-mean-squared error (RMSE), possibly weighted by the cube of the
wind speed, should be calculated for each set of parameters estimated.
o Extreme value analysis using the Gumbel distribution. This distribution is useful for
predicting the likelihood of extreme wind speed events based on a set of observed
maximum wind speeds. Like the Weibull distribution, the Gumbel distribution is
characterized by two parameters.
o Calculation of confidence intervals. It would be useful to have stated confidence limits
around the estimated mean wind speeds and, more importantly, the mean wind power
densities. The lower bounds of these limits could be used in conservative financial
analyses to determine expected cash flows from electricity production at given levels of
confidence; 90% confidence is often used in analyses of debt repayment. Care must be
taken in calculating these confidence intervals as the underlying data distributions are
decidedly non-normal.
o Measure-Correlate-Predict (MCP) analysis. MCP analysis is often used in wind resource
analysis to compare a potential wind development site to a remote site of long-term
wind data collection. Concurrent short-term wind measurements from the two sites are
correlated and used to develop a statistical model of the relationship. Then the model is
used with the historical data of the reference site to ‘predict’ the long-term historical
wind resource at the development site.
o Error analysis. Integral to the utility of any statistical analysis, an error analysis would
quantify the random variation included in the results as a consequence of the
inaccuracies of the sensors, data collection equipment, and processing methods.
• Comparison of the wind resource study results to standards developed by the International
Electrotechnical Commission (IEC) for wind turbine design and construction. IEC Standard
61400-1 establishes design criteria for wind resource characteristics used by wind turbine
manufacturers. In order to help establish the suitability of the site for wind turbine
development, the wind study results could be compared to the IEC design criteria for the
following:
o wind turbine class
o extreme wind speed events
o wind shear
o turbulence intensity
37
Appendix A
Unit Conversions
Distance: 1 ft = 0.3048 m, 1 mi = 1.6093 km
Pressure: 1 atm = 14.696 psi = 29.921 inHg = 101,325 Pa = 1013.25 hPa = 1013.25 mbar
Temperature: °F = °C x 1.8 + 32, K = °C + 273.15
Velocity: 1 KT = 0.5144 m/s, 1 m/s = 2.2369 mph
Data Logger Channels
Chan. Sensor Description Serial
Number
Height[1]
Scale
Factor
Offset Print
Precision
Units
1 Anemometer Top North SN:47003 102 m 0.759 0.34 1 m/s
2 Anemometer Top South SN:46350 102 m 0.762 0.32 1 m/s
3 Anemometer Mid North SN:47001 81 m 0.759 0.33 1 m/s
4 Anemometer Mid South SN:47002 81 m 0.759 0.34 1 m/s
5 Anemometer Low North SN:46348 52 m 0.761 0.30 1 m/s
6 Anemometer Low South SN:46349 52 m 0.763 0.31 1 m/s
7 Wind Vane Top DC:0152 102 m 0.351 0 0 deg
8 Wind Vane Low DC:0152 52 m 0.351 0 0 deg
9 Temperature Top SN:N/A 102 m 0.136 -86.379 1 °C
10 Temperature Ground SN:N/A 2 m 0.136 -86.379 1 °C
11 Humidity Ground SN:N/A 2 m 0.097 0 1 %RH
12 Barometric Pressure 18054873 2 m 0.4255 666.94[2]
2 hPa [1]
Height above site elevation of 142 m (466 ft). [2]
648.77 (calibrated offset) + 18.17 (sensor height above sea level, 144 m (473 ft)) = 666.94 (final offset)
38
References
[1] IIRA. 2007. Illinois Wind Monitoring Program. Illinois Institute of Rural Affairs. Western Illinois University.
Macomb, IL. Illinois wind resource maps available at http://www.illinoiswind.org/windData/maps.asp, last
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[2] GEM. 2004. Structural Analysis Report, Site Number CAR-056, GEM Project 981849. Global EnerCom
Management, Inc., Houston, TX. September 29, 2004.
[3] NCDC. ASOS 5-minute surface data, DSI 6401. http://www.ncdc.noaa.gov/oa/climate/climatedata.html ,
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[5] Ibid.
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[10] Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E. 2001. Wind Energy Handbook. John Wiley & Sons, Ltd.
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[11] IEC. 2005. IEC 61400-1:2005, Wind Turbines – Part 1: Design Requirements. 3rd
ed. International
Electrotechnical Commission. Geneva, Switzerland. p. 24.
[12] Bailey, B., and McDonald, S. 1997. Wind Resource Assessment Handbook. AWS Scientific, Inc., Albany, NY.
Prepared for National Renewable Energy Laboratory, Golden, CO. NREL Subcontract TAT-5-15283-01.
p. 9.6.
[13] IEC. 2005. IEC 61400-1:2005, Wind Turbines – Part 1: Design Requirements. 3rd ed. International
Electrotechnical Commission. Geneva, Switzerland. p. 26.
[14] Bailey, B., and McDonald, S. 1997. Wind Resource Assessment Handbook. AWS Scientific, Inc., Albany, NY.
Prepared for National Renewable Energy Laboratory, Golden, CO. NREL Subcontract TAT-5-15283-01.
pp. 9.1 – 9.6.
[15] NCDC. Integrated Surface Hourly Dataset, DS3505. Station Name: “Southern Illinois”. USAF: 724336.
http://www.ncdc.noaa.gov/oa/climate/climatedata.html#hourly, accessed 5/11/2011.