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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**************
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PlanIntroduction ProblematicEnergy MeteorologyMethodologyResults & DiscussionsConclusions & Perspectives
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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,
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Introduction
4 Fig. 0.1. Temperature changes since 1880
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Study Location
5 Fig. 0.2. Map of Cameroon showing study sites
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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
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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
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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
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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
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Leaders in wind installation in the world
Fig. 1. Leading World Countries in installed Wind Power
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Fig. 0.6. World wind energy installations
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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
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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
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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
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Physics Meteorology
Wind at higher heights governed by:
(1.1)
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Wind shear profile
Fig. 1.1. Wind shear profile 15
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Vertical Extrapolation of Wind speed
Many expressions but most prominently used are:
(i): The log-law (1.3) (ii): The power law (1.4)
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Types of wind turbines
Fig. 1.2. Horizontal axis turbine (HAWT) Fig. 1.3. Vertical axis turbine (VAWT) 17
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Wind turbine characteristics power curve
18Fig. 1.4. Power curve characteristics of a wind turbine
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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)
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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
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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)
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Weibull and Rayleigh PDF Models
(i) Weibull PDF & CDF (2.4) (2.5)(ii) Rayleigh PDF & CDF (2.6)F (2.7)
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Gamma PDF Model
Gamma PDF (2.9)
Gamma CDF(2.10)
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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
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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)
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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
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Modeling Power density
(i): Weibull power density (2.26)
(ii): Rayleigh power density (2.27) (2.28)
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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
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Time series variations Yoko
Fig. 3.1. Time series wind speed variations for Yoko for 6769 data
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Weibull and Rayleigh PDFs
Fig. 3.2. Histogram & PDFs for Yoko for 6769 data
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Weibull and Rayleigh CDFs
Fig. 3.3. Monthly CDFs for Yoko for 6769 data
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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
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Monthly PDFs for 1st Year data set
Fig. 3.4. Monthly PDFs for Yoko for 6768 data
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Monthly CDFs for 1st Year data set for 1st Year
data set
Fig. 3.5. Monthly CDFs for Yoko for 6768 data
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Wind Regime Pattern Assessment
Fig. 3.5. Comparing PDFs for Wind Regime Pattern representativeness
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Wind rose plots for Bafoussam
Fig. 3.6. Wind rose plot for Bafoussam for 2007
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Surface roughness of Bafoussam
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Power density atlas of Cameroon
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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.
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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.
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Thank you for your keen attention