VARIATION AND ASSESSMENT OF WIND ENERGY POTENTIAL AT ... · showed that the Coefficient of Variation(CV) for a 10 year wind speed data range from 18.35% to 51.95% .These values exceeded

Proceedings of 44th ISERD International Conference, Dubai, UAE, 1st August 2016, ISBN: 978-93-86083-80-7

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VARIATION AND ASSESSMENT OF WIND ENERGY POTENTIAL AT ABEOKUTA, NIGERIA BASED ON WEIBULL DISTRIBUTION.

1SODUNKE, M. A., 2ADEWALE,A.O ., 3ALABI,A.A, 4MABOSANYINJE,A

1,2Physics/Electronics unit, Department of SLT, Moshood Abiola Polytechnic, Ogun State , Nigeria.

3Department of Physics, University of Agriculture, Abeokuta, Ogun State , Nigeria. 4Department of Statistics and Mathematics , Moshood Abiola Polytechnic, Ogun State , Nigeria.

E-mail: [email protected]

Abstract- In this study , a statistical analysis of wind speed was carried out at Abeokuta station in Nigeria, by finding the coefficient of variations and using Weibull distribution for a 10 year wind data .The data was analyzed and the results showed that the Coefficient of Variation(CV) for a 10 year wind speed data range from 18.35% to 51.95% .These values exceeded the variability threshold of 10% indicating that they exhibit significant variability. The shape factor and scale factor, which are the parameters of the Weibull distribution ,the mean wind speed, probability density function and their cumulative distribution were determined for all the years .The results of the analysis indicated that the city exhibits a low wind since the probability density function falls between 22.0 w/m2 and 160.9w/m2 of wind power class one and wind power class three respectively .The energy could be efficiently utilized to generate electricity if the turbine is erected and installed properly. The wind speeds regimes for a 10 year wind data analyzed showed power density ranging between 22.0 w/m2 and 160.9w/m2. Ultimately, the results of this study shows that Abeokuta is a low wind region. However, the low wind values that characterize Abeokuta could be effectively utilized as long as the turbines’ heights are in between 50 meters and 1500 meters respectively. Keywords- Coefficient of variation, Weibull distribution, Probability density function, Cumulative distribution function, Scale factor, Shape factor. I. INTRODUCTION The convectional energy supply, produced through large scale, highly centralized production and distribution units ,has not been equally distributed and has not been equally distributed and has often proved to be inadequate in meeting the economic needs of the rural areas this inadequacy in energy supply is being met by the use of fuel wood and manual power .The poor standard of living resulting from lack of viable energy sources and decreasing energy utilization has led to rural –urban drift .A potent integrative factor for stemming the rural-urban migration is the provision of basic social amenities such as electricity ,water supply and telecommunications in the rural areas. Economic factors often militate against the provision of such amenities in these scattered rural settlements. In 1989,the Directorate of Food, Road and Rural Infrastructure(DFFRI), which the Nigerian government set up to develop these facilities, identified about 86,785 communities and villages all over the country which have been connected to the national grid due to the prohibitive cost of doing so. To stem these migrations ,we need to exploit and optimize the use of all available sources of energy in providing a reliable and adequate power supply for the industrial and economic development of the rural communities[12]. Wind speed is a common factor in the design of structures and buildings around the world .The wind speed is often the governing factor in the “lateral” design of a structure and is used by professional engineers and designers. Wind speeds regimes in Nigeria are so moderate and this has made

hurricane and gale forces relatively uncommon. In Nigeria, wind power has so far received only limited attention even though there are some Wind Energy Conversion Systems (WECS).These are used for various purposes but were installed .Subjectively, without proper knowledge of the wind distribution in respective areas in which they are operating .As a result, such systems where ignored and most of them were destroyed .Detailed knowledge of the wind characteristics is essential to allow optimizing the design and the usage of WECS. This will be accomplished by using wind speed measurements .Bur this is a very difficult task due to transitions in directions and speed of wind in most sites .Many studies have been carried out to estimate wind potential in different parts of the world. The work on wind data collection could be made possible in Nigeria through Nigeria Meteorological Agency (NIMET) (Sodunke et al., 2011) .

The role of wind in an economy cannot be undermined since its an important tool in power generation .Energy is a basic element of any economic development and electric energy is an important index of a country’s economical and technological progress [18 ]. The work on wind data collection could be made possible in Nigeria through Nigeria Meteorological Agency (NIMET) [16]. Nigeria has a total of 44 weather stations that routinely measure climate parameters like solar radiation, sunshine, hours temperature, vapour pressure, wind speed and its direction and humidity. Despite routine meteorological data ,many overestimate wind speed

Variation and assessment of wind energy potential at abeokuta, nigeria based on weibull distribution.


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at a specific site, the tentative wind atlas obtained from this study have recognized the apparent potential and provided valuable general information[9,13]. Generally, the wind energy is characterized by a high variability both in space and time. It is therefore very important to describe the variation in wind speeds for optimizing the design of the systems in order to reduce energy generating costs. In recent times, numerous studies have been carried out to access the wind speed characteristics and associated wind energy potentials in different parts of the world[1,2,3,4,5,7,8,10,11,15,9,18,19] The objective of this study is to assess and investigate the quality of wind energy potential in Abeokuta

towards electricity generation . The Weibull distribution is well suited for this study since it is a handy tool for the calculation of parameters relevant to wind power generating systems. In statistical modeling of the wind speed variation, much consideration has been given to the weibull two parameter (shape parameter K and scale parameter C) functions because it has been found to fit a wide collection of wind data [3].

II. STUDY AREA The study area is situated at the latitude of 70.031N and longitude of 03.191E.It is the largest city and the capital of Ogun State. It was connected to the national grid in 1976.

Fig 1.0 The map of Nigeria showing the position of Abeokuta in Nigeria.

III. WIND DATA COLLECTION AND EVALUATION Wind data was collected from the archive of Nigeria Meteorological Agency(NIMET) for a period of 10 years covering Abeokuta station. The rate of the data recording was 144 per day in 10minutes intervals. The collected data included date and time stamp, minimum, maximum, average and deviation values of wind speeds at different measurement height ranging from 1.59m to 7.09m, wind directions divided in 16 sectors (within 360), temperature and relative humidity. Once the required data was stored in the

data logger, it was sent directly via a GSM method to NIMET in Oshodi where it was necessary to evaluate the percentage of the weather or the malfunctioning of the instrumentation. It has been found that in overall the missing data did not exceed 70% a percentage well within the acceptable standards. After establishing this, the analysis of the recordings was performed and the corresponding weibull distribution was determined. Additionally, the yearly wind speed variation was obtained in order to check the validity of the data and to extract any useful information regarding the wind potential of the location under consideration. Hence data were used to



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evaluate frequency of a certain wind speed as well as the monthly and annual mean wind speed. Two different statistical software were used Excel spreadsheet and Matlab. Excel spreadsheet was used to calculate the mean wind speed, shape factor, scale factor, probability density function and cumulative distribution function while Matlab was used for the plotting of the probability density function and cumulative distribution function graphs. IV. METHODOLOGY AND MATERIALS SITE DESCRIPTION Abeokuta is the largest city and capital of Ogun State in Southwest Nigeria. It is situated on the east bank of the Ogun River, near a group of rocky outcrops in a wooden savanna. Abeokuta lies in fertile country of wooden Savanna, the surface of which is broken by masses of grey granite. It is spread over an extensive area, being surrounded by mud walls 18 miles in extent . V. THEORETICAL BACKGROUND The coefficients of Variations were computed in order to investigate the month-month variability of the meteorological parameters. The coefficient of variation is defined as (Murat et al.,1995): CV = 100% ( 1 ) Where MP stands for monthly mean of any of the wind speed and σ is the standard of deviation for the monthly wind speed regimes. VI. PROBABILITY DENSITY FUNCTION Wind speed for a given location can be characterized by several probability density functions. For wind data analysis, the Weibull and Rayleigh probability density functions are commonly used and widely adopted. Here, the Weibull density function is used to describe the wind speed frequency distribution. The Rayleigh distribution is a special case of the Weibull distribution. The general form of the parameter Weibull probability density function is mathematically expressed by F (v) = (k/c) (v/c) K -1 exp ( -(v/c) k) ( 2 ) (k.>0,V>0,C>1) Where F (v) is the probability of having a wind speed of V (m/s), K is dimensionless shape factor and C is the Weibull scale factor with units of speed (m/s), which could be related to the average wind speed through the shape factor, which describes the distribution of the wind speeds [3,19 ]. The relationship between the Weibull .Scale factor C, Weibull shape factor and average wind speed V m is given by the following formula. V m = C г (1 + 1/K) ( 3 ) Where г is the usual gamma function .The parameters K and C may be estimated by the linear regression of the cumulative Weibull distribution given by

F (v) = 1 –– exp ( - (v/c) K ) ( 4 ) [3,15,18] VII. VARIATION OF WINDSPEED WITH HEIGHT Wind speed generally changes with height, which requires an equation that predicts the wind speed at one height in terms of the measured at another height .Under normal conditions, wind speed is greater at higher distance above ground .This is largely because the effect of surface features and turbulence diminishes as height increases .The variability depends on distance from the ground and roughness of the terrain. The most common expression accepted to describe the vertical variation of wind speed is expressed by a power law having the formula below V/VO = (H/HO)α ( 5 ) Where V is the wind speed at the required height, VO is the wind speed at the original height HO and α is the surface roughness coefficient .The value of the exponent α varies from less than 0.10 over the tops of steep hills to over the 0.25 in sheltered locations .The typical value for flat coastal regions is 0.143, which is termed the one – seventh power law from which measurements at different height the exponent can be determined [12, 18]. VIII. WIND ENERGY POTENTIAL At a wind speed V, the available energy per unit area perpendicular to the wind stream over a given period of time t is expressed by kinetic energy [18]. Ea = 0.5ρ v3 ( 6 ) Where p is the air density (kg /m3) and EO is the theoretical total energy available for doing work on the turbine .However, only a fraction of the total energy would be extracted .The maximum extractable energy from a system working at its optimum efficiency is limited by a coefficient of performance called Beltz limit (16/27 = 0.593).This capacity factor makes the extractable energy approximately 59.3% of the theoretical energy and is given by EM = 0.2965ρv3 ( 7 ) IX. ENERGY IN THE WIND Wind is merely air in motion. It is produced by an uneven heating of the earths surface by energy from the sun. Since the earth’s surface is made at different types of land and water, it absorbs radiant energy at different rates. Much of this energy is converted into heat as it is absorbed by land areas, bodies of water and the air over these formations. The air has mass ,though its density is low and when this mass has velocity ,the resulting wind has kinetic energy which is proportional to ½ ( mass X ( velocity)2 ).The mass of air passing in unit time is ρAV and the



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kinetic energy passing through the area in unit time (power available in the wind) [3].

PW = ½ ρ AV 3 ( 8 ) X. RESULTS

Figure 1.1: Coefficient of Variations from 1998 to 2007

Figure 1.2: Mean Monthly wind Distribution from 1998-2007

PROBABILITY DENSITY GRAPHS FROM 1998-2007

Figure 2..0: Probability Density Function for 1998

Figure 2.1: Probability Density Function of 1999

Figure 2.2: Probability Density Function for 2000






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CUMMULATIVE DISTRIBUTION FUNCTION GRAPHS FROM 1998-2007

Figure 3.0: Cumulative Distribution for 1998

Figure 3.1: Cumulative Distribution Function for 1999





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TABLE 1.01: COEFFICIENT OF VARIATIONS FOR THE

MONTHS FROM 1998-2007

TABLE 1.02 MONTH OF HIGH WINDSPEED FOR THE STATIONS



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TABLE 1.03: WEIBULL DISTRIBUTION PARAMETES,

MEAN WIND SPEED AND WIND POWER DENSITY

DISCUSSION Figures 1.1 and 1.2 : show the Coefficient of Variations and Mean monthly wind distribution of wind speed from 1998 to 2007 .The for 10 years wind speed regimes range from 18.35% to 51.94%.FromCV (Table 1.01): it is evident that the least CVwas recorded in February while May recorded the highest CV. Months of March and November recorded the highest and lowest wind speed values ranging from 4.69m/s to 3.39 m/s as shown in Table 1.02. Weibull distribution was applied in the wind speed data in order to take care of the limitation of the coefficient of variation in predicting the reliability of the wind speed regimes. Figures 2.0 – 2.9 show the probability density functions which indicate the fraction of time for which a wind speed possibly prevails at the area under investigation. The distribution of wind speed is high in 2003 compared to other years. One of the aims of this study is to correlate and compare the results of qualitative studies with statistical analysis of a 10 year wind speed data (1998-2007). The results show that for a 10 year period of the wind regimes under study, the mean wind speed range from 5.4m/s to 6.6m/s. Year 2003 exhibited the highest wind speed of 6.6m/s while the lowest wind speed of 3.4 m/s was experienced in 2007. In addition, Weibull is a statistical tool which tells us how often winds of different speed will be seen at a location with a certain mean wind speed, shape factor and scale factor. The shape factor is indicative of a normal wind distribution of wind power obtained .Knowing the shape factor helps to choose a wind turbine with the optimal cut in speed (the wind speed at which the turbine start to generate usable power) and the cut out speed (the speed at which the turbine hits the limits of its alternator and no longer puts out increase power output with further increase in wind speed). The standard cut in speed of most machine is 4.5 m/s and the cut out speed is 25 m/s).The scale factor is a parameter of the weibull function that increases with height and this helps to determine how valuable a wind speed could be of importance to wind energy community.

As shown in the histograms the wind speed with high shape and scale factor has high mean wind speed and gives high wind potential of good quality. Figures 3.0 – 3.9 show a cumulative distribution function which indicates the fraction of time the wind speed is below a particular speed by taking the difference of its values and the corresponding time for which the turbine would be fractional and can be estimated i.e the cut in and cut out speed. Considering a typical machine with cut in speed of 4.5 m/s and rated speed of 8.0m/s on the basis of the cumulative distribution frequency. In 2003, a wind turbine with the above specifications would be a stand still for about 3% and operate for about 97% of the time at partial load. Furthermore, in years 1988,1999,2000, 2001,2002,2004 and 2005, a wind turbine with the above specification would be a stand still for about 13% and operate for about 87% of the time at partial load while for 2006 and 2007,20% of the wind speed was less than 4.5m/s and this is relative to 80% output of the cut in and cut out speed. Considering a vesta V42 with 4.5m/s of cut in speed, the wind regimes of 2006 and 2007 could be well applicable while that of 2003 would utilize the machine perfectly. The frequency distribution of wind speed is essential in the wind quality assessment. CONCLUSION The monthly coefficient of variations and the yearly measured time series wind speed data for Abeokuta station in Nigeria have been analyzed statistically based on Weibull probability distribution function. The yearly Weibull probability distribution parameters, mean wind speeds and wind energy density availability for the location have been determined respectively. Based on the analysis the following conclusions can be made: (a)All the wind speed regimes (1998-2007) showed a marked degree of variations, having exceeded the 10% threshold of reliability and revealed evidently that there exists significant variability in these wind speeds investigated. (b) Years 1998, 2002, 2004, 2005, 2006 and 2007 belong to wind power class one with power density ranging from 22.0w/m2 to 98.1w/m2. (c) Wind power class two was exhibited in the years 2000 and 2001with power density ranging from 109.1w/m2 to 115.0w/m2, Year 2003 belongs to wind power class one and depicted 160.9w/m2 power density. (d) This study shows that within a period of ten years, from 1998 to 2007, wind energy was mostly available in the year 2003 alone. (e) For effective use of the wind electricity in the studied years, the turbine could have been best installed at 50metres for 1998,1999,2000,2001,2002,2003,2004 and 2005.



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Heights 1000 meters and 1500 meters would have been suited for turbine installation in 2006 and 2007 respectively. Finally, proper erection of turbines is recommended for efficient use of turbines. (f) Ultimately ,the results of this study shows that Abeokuta is a low wind region. However, the low wind values that characterize Abeokuta could be effectively utilized as long as the turbines’ heights are in between 50 meters and 1000 meters and 1500 meters respectively. REFERENCES [1]. Adam, Hugh Monaham (1990): Solar and Wind Technology,

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Wind Energy Potential on the Basis of the Weibull and Rayleigh Distribution.Pages 218,557-565.

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[5]. Chang Y.T,HsunC.R, and Liao(2003):Assessment of wind Characteristics and Wind Turbine Characteristics in Tiawan.Volume 28 Pages 851-857.

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[9]. Iloeje ,O.C (2002): Renewable Energy Development in Nigeria. Proceedings of National Workshop of Energizing Rural Transformation in Nigeria.

[10]. Isaac Y.F. Lun and Joseph C.L. (1999): A Study of Weibull Parameter using long term wind observation, 1999, Pages 1 – 2.

[11]. Matthew,PandeyK and PandKumar,V.A (2002):Analysis of Wind Regimes Estimation, Volume 25,Pages 281-399.

[12]. Murat .T,Utku,M. Summer and Gonul Kilic(1995):”Variations and Trends in Annual Air Temperatures in Turkey with respect to climatic Variability”. International Journal of Climatology, Volume 15,pages 557-564.

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[14]. Records of wind data. Meteorological Department, Oshodi, Nigeria.

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[17]. Sodunke, M.A et al.,(2011):The Study of Wind Energy Potential in the Northern Region of Nigeria, Journal of Engineering and Applied Sciences, Volume 3,Pages 29-42.

[18]. Weisser ,D(2003):A Wind Energy of Analysis of Grenada –an Estimation using the Weibull Density Function,Volume 28,Pages 1803-1812.

[19]. Youm, J. Sarr and M. Sall (2005): Analysis of Wind data and Wind Energy Potential along the northern cost of Senegal, Volume 8, Pages 95 – 108.

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VARIATION AND ASSESSMENT OF WIND ENERGY POTENTIAL AT ... · showed that the Coefficient of Variation(CV) for a 10 year wind speed data range from 18.35% to 51.95% .These values exceeded