DEPARTEMENT DE PHYSIQUE

DEPARTMENT OF PHYSICS 

UNIVERSITY OF DSCHANG***************

POSTGRADUATE SCHOOL*************

DOCTORAL TRAINING UNITFUNDAMENTAL SCIENCES AND

TECHNOLOGY***********

 

Laboratory of Mechanics and Modelling of Physical Systems (L2MSP)Contribution to the investigation of wind characteristics

and assessment of wind energy potential for some regions in Cameroon

  THESISSubmitted in partial fulfillment of the requirements for the award of

Doctorat/PhD in PhysicsOption: Mechanics-Energetics

  

ByBAWE Gerard NFOR, Jr.

Registration number: CM04-09SCI4256MSc (Exploration Geophysics)

  

Under the co-supervisions of TALLA Pierre Kisito YEMELE David Associate Professor Associate Professor University of Dschang University of Dschang

2016  

 

1

UNIVERSITÉ DE DSCHANG************

ECOLE DOCTORALE*************

UNITE DE FORMATION DOCTORALESCIENCES FONDAMENTALES ET

TECHNOLOGIE**************

 

PlanIntroduction ProblematicEnergy MeteorologyMethodologyResults & DiscussionsConclusions & Perspectives

2

Introduction

Energy permeates all the fabrics of our daily activities

And primordial as the live wire of industries Industries offer employment opportunities and

better standard of living However, mostly fossil based fuels are used and

are identified with undesirable characteristics Emission of GHG (global warming), SO2 (acid

rain) and also used as instruments of coercion (blackmail and wars) and depletion,

3

Introduction

4 Fig. 0.1. Temperature changes since 1880

Study Location

5 Fig. 0.2. Map of Cameroon showing study sites

Energy Demand Factors

6

1970 1980 1990 2000 2010 20200.05.0

10.015.020.025.0

Population growth

year

popu

lati

on (

mil-

lions

)

Fig. 0.3. Population growth

1975 1980 1985 1990 1995 2000 2005 2010 20150.001.002.003.004.005.00

Total Hydroelectricity Net Generation

year

Ener

gy (

BkW

h)

Energy Situation in Cameroon

7 Fig. 0.4. Hydroelectricity production

8

HE station Capacity (MW)

Year completed

Name of reservoir River

Edea PS 204 1953 Edea Reservoir Sanaga River

Song Loulou PS 384 1981 &

1988 Song Loulou Reservoir

Sanaga River

Lagdo PS 72 1982 Lagdo Reservoir Benue River

Memve'ele PS 200 2013 Memve'ele

ReservoirNtem River8

Energy Situation in Cameroon

19751980198519901995200020052010201520200.050.0

100.0150.0200.0

Cameroon: Production of Crude Oil

year

Prod

ucti

on (

Thsn

d ba

rrel

s/da

y

Energy Situation in Cameroon

9 Fig. 0.5. Crude oil production

Leaders in wind installation in the world

Fig. 1. Leading World Countries in installed Wind Power

10

Fig. 0.6. World wind energy installations

11

CountryTurbines

Capacity (MW)

Algeria 1 11Cape Verde 5 31Egypt 9 745Eritrea 1 1Ethiopia 3 325Gambia 1 1Libya 1 20Mauritania 2 36Mauritius 1 2Morocco 13 885Mozambique 1 1Namibia 1 1Nigeria 1 11Seychelles 1 6South Africa 21 1,670Tanzania 1 50Tunisia 3 243

Table 0.1. Wind installation in Africa

Problematic

Frequent electricity cuts We create wind speed data bank of these areas Carry out comparative studies of best

representative of some PDFs, introducing the new MEP-type probability density function

Estimate wind energy potential at study sites Produce wind speed and Power density atlases

12

Chapter one:ENERGY METEOROLOGY

PHYSICS METEOROLOGYStratified atmosphere, ours is the troposphereEarth surrounded by a blanket of air Interested in air in the lower 100m; the ABLWind is Air in motionProduced by differential heating of the earth

13

Physics Meteorology

Wind at higher heights governed by:

(1.1)

14

Wind shear profile

Fig. 1.1. Wind shear profile 15

Vertical Extrapolation of Wind speed

Many expressions but most prominently used are:

(i): The log-law (1.3) (ii): The power law (1.4)

16

Types of wind turbines

Fig. 1.2. Horizontal axis turbine (HAWT) Fig. 1.3. Vertical axis turbine (VAWT) 17

Wind turbine characteristics power curve

18Fig. 1.4. Power curve characteristics of a wind turbine

Wind power equation Formulation of transformation of kinetic energy of

the wind to electrical power (1.5) 𝑚=𝜌𝐴𝑣 (1.6) (1.7) Eqn (1.7) is the available power presented to turbine However, Betz’s law limits it to a maximum of =

59.3% of (1.8)

19

Chapter Two: Materials and Methodology

Materials mostly software: Matlab R2013b, MS Excel 10 and QGIS Sites found on next page Two years (731 days) of daily mean wind speed, using cup

anemometers, from 22 sites and in Excel format Hard copies of 7 years of 3-hourly separation time steps, from

6am to 6pm, daily, from Bafoussam Airport, using Beaufort scale

Preprocessing of wind speed for completeness, Statistical analysis and Modeling of wind speed 2

0

Data Processing

Vertical extrapolation: Where necessary, and for convenience, we use the power law (2.1)Statistical Analysis(i) Mean wind speed: (2.2)(ii) Standard deviation: (2.3)

22

Weibull and Rayleigh PDF Models

(i) Weibull PDF & CDF (2.4) (2.5)(ii) Rayleigh PDF & CDF (2.6)F (2.7)

22

Gamma PDF Model

Gamma PDF (2.9)

Gamma CDF(2.10)

23

Lognormal PDF Model

The Lognormal given by equation (2.11)

(2.11)

𝜎 and are the standard deviation and 𝜇mean of the logarithm of the wind speed

24

Maximum Entropy Principle (MEP)

With constraints equated to 1, mass, momentum and kinetic energy, respectively, we obtain these equations:

(2.14) (2.15) (2.16) (2.17)

25

Goodness of fit (GoF) tests

(i): Coefficient Of determination (COD) or R2 (2.23)(ii): Root-mean square error RMSE= (2.24)(iii): Chi square (2.25) Highest R2, lowest RMSE and X2 implies good accord of the model

for the wind speed regime

26

Modeling Power density

(i): Weibull power density (2.26)

(ii): Rayleigh power density (2.27) (2.28)

27

Chapter ThreeResults and discussions

This chapter presents the results of the computations and simulations for the parameters theoretically explored in the former chapter.

For ease of presentation, legibility and comprehension they shall mostly be graphics and tabulations followed by commentaries or explanations.

However, only the results of Yoko are presented in detail For the remaining sites, only some results displayed,

particularly for comparison and general appraisal Most of the tables and figures are relegated to the

appendix

Time series variations Yoko

Fig. 3.1. Time series wind speed variations for Yoko for 6769 data

Weibull and Rayleigh PDFs

Fig. 3.2. Histogram & PDFs for Yoko for 6769 data

Weibull and Rayleigh CDFs

Fig. 3.3. Monthly CDFs for Yoko for 6769 data

Numerical results for Yoko

Model PDF k

c M SRMSE R2 X2

PD WP RP WP RPm/s W/m2 %

Weibull

4.1 4.03.7 0.9

0.0439

0.9959

0.0033 33.

834.7

54.2 2.6 60.

5Rayleigh

2.0 2.7 0.1497

0.9524

0.0388

Monthly PDFs for 1st Year data set

Fig. 3.4. Monthly PDFs for Yoko for 6768 data

Monthly CDFs for 1st Year data set for 1st Year

data set

Fig. 3.5. Monthly CDFs for Yoko for 6768 data

Wind Regime Pattern Assessment

Fig. 3.5. Comparing PDFs for Wind Regime Pattern representativeness

Wind rose plots for Bafoussam

Fig. 3.6. Wind rose plot for Bafoussam for 2007

Surface roughness of Bafoussam

Power density atlas of Cameroon

Conclusion Cameroon suffers from severe power crisis and there is dire need for a

solution. In an attempt to curb with the situation, Cameroon has embarked on thermal plants. However, there is an outcry against the use of fossil fuels because of environmental concerns. There is therefore need to search for sustainable alternatives such as wind energy.

Based on available data, we studied the wind energy potential of 22 sites and also carried out wind regime representativeness comparing five probability density functions. Finally we produced the power density map of the country.

Based on the data, our results show that Cameroon is a very poor candidate for commercial wind energy exploitation; for all the sites fell under category 1 of the wind speed and/or power density class. However, Yoko, Betare Oya and Bafia prove to be exploitable for low electric power appliances and water pumping. It was also observed that any of the PDFs could be used to describe the wind regime as the overall least R2 was 94%.

Wind rose plots determined winds in Bafoussam mostly flow in from angle 10o, in accordance with its surface roughness.

Perspectives Using the old and the new data from Bafoussam, we shall use neural

network to try to generate and obtain the present from the former so as increase the reliability of using the former today

A reliable power density map should be produced from data from as many sites as possible. Hence, it is imperative to obtain data from many sites so as to give the density atlas a better meaning.

Only two years data length was used in this study. This is highly insufficient for a any exploration for commercial exploitation. Hence, if not now, this exercise should be repeated, at least in the next ten years for better statistically sane picture of the results.

Proper siting of the meteorological stations is of paramount importance. This point is pertinent because Bamenda is in a fence, while Dschang’s is found amidst very tall buildings.

Thank you for your keen attention