COMPARISON OF FIVE NUMERICAL METHODS FOR ESTIMATING WEIBULL PARAMETERS FOR WIND ENERGY APPLICATIONS IN THE DISTRICT OF KOUSSERI, CAMEROON

There is a crucial need in the Northern regions of Cameroon to enhance the development of wind technology and engineering, which can be considered to design and characterize Wind Energy Conversion Systems (WECS). The Weibull Probability Density Function (PDF) with two parameters is widely accepted and commonly utilized for modeling, characterizing and predicting wind resource and wind power, as well as assessing optimum performance of WECS. Therefore, it’s crucial to precisely estimate the scale and shape parameters for any candidate site. The statistical data of 28 years (1985-2013) wind speed measurements in the district of Kousseri were analyzed and the Weibull parameters determined. The performance of the proposed five methods was carried out based on the correlation coefficient R2 and root mean square error (RMSE). The results established that the proposed five methods are effective in evaluating the parameters of the Weibull distribution for the available data. However, the most accurate models are the energy pattern factor method followed by the maximum likelihood method and the graphical method. The least precise models are the modified maximum likelihood method and the empirical method. Keywords: Weibull distribution, Maximum likelihood method, modified maximum likelihood method, graphical method, energy pattern factor method. INTRODUCTION The prospect of energy in Cameroon is challenging. With fast increasing energy demand, in addition to the growing depletion of natural resources and rising environmental consciousness, it has become crucial to enhance local energy supply with most promising renewable sources of energy. Wind, an inexhaustible resource, is a reliable renewable energy resource from a standpoint of long-term energy policy. Although wind energy is intermittent, wind turbine generators can effectively reduce environmental pollution, fossil fuel consumption, and the costs of overall electricity generation in the district of Kousseri. As a random phenomenon, wind speed is the most significant parameter of the wind energy. Therefore an accurate determination of the probability distribution of wind speed is essential for predicting the energy output of a WECS. In the last few years, researches in the wind engineering field and wind energy industry have devoted to the development of suitable predictive models to describe wind speed frequency distribution. The two-parameter Weibull PDF has been used to represent wind speed distributions for applications in wind loads studies [1]. In addition, the Weibull PDF has been Asian Journal of Natural & Applied Sciences Vol. 3(1) March 2014 ________ ____________ ____________ ____________ _____ ____________ ____________ ____________ ____________ ___________ ____________ ____________ ____________ _____ ____________ ____________ ____________ ___________ ____________ ____________ ____________ ___________ ____ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ ___________ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ _____ ______ ___ Copyright © 2014 Leena and Luna International, Oyama, Japan. 73 | P a g e (株) リナアンドルナインターナショナル, 小山市、日本. ISSN: 2186-8476, ISSN: 2186-8468 Print www.ajsc. leena-luna.co.jp found as a useful and appropriate method of computing power output from wind-powered generators and applied to estimate potential power output at various sites across the continental United States [2]. In a study, Lysen [3] stated that the Weibull PDF showed its usefulness when the wind data of one reference station were used to predict the wind regime in the surroundings of that station. Patel [4] claimed variations in wind speed are best described by the Weibull PDF with two parameters. There seems to be a compromise in the literature that the Weibull PDF with two parameters, the dimensionless shape parameter k, and the scale parameter C, is a good quality probabilistic model for wind speed at one location. It is obvious that the more appropriate Weibull estimation method shall provide accurate and efficient evaluation of wind energy potential. In this regard, a number of studies have been carried out by various researchers in order to assess wind energy potential by using the Weibull PDF [5-9]). Various methods have been effectively experimented for estimating the shape and scale parameters and the suitability of each method ranged according to the sample data distribution, which is basically location specific. In the present study, five numerical methods, namely, the maximum likelihood method, the modified maximum likelihood method, the energy pattern factor method, the graphical method, and the empirical method are explored and their suitability compared for the district of Kousseri located in the Far North Region of Cameroon. The data collected for this study, were up to three times-a-day synoptic observations during the period from 1985 to 2013. The aim of this work was to select a method that gives more accurate estimation for the Weibull parameters at this location in order to reduce uncertainties related to the wind energy output calculation from any Wind Energy Conversion Systems (WECS). MATERIALS AND METHODS Data source The data provided for the study were up to three times-a-day, randomly measured synoptic observations during the period from 1985 to 2013. The synoptic station is located as described by the geographical coordinates in the table 1. The table 2 shows the monthly mean wind speed and standard deviation of the measured data while the monthly wind speed frequencies distributions are described by the figure 1. Table 1. Geographical coordinates of the study area Variable Value Latitude 12°04’60” N Longitude 15°01’59”E Anemometer Height 10 meters height above ground level Elevation 302 meters above sea level Asian Journal of Natural & Applied Sciences Vol. 3(1) March 2014 ________ ____________ ____________ ____________ _____ ______ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ _____ ______ ______ ______ ______ ______ ______ ______ _____ ____________ ____________ ____________ ___________ ____ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ ___________ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ _____ ______ ___ ISSN: 2186-8476, ISSN: 2186-8468 Print www.ajsc.leena-luna.co.jp Leena and Luna International, Oyama, Japan. Copyright © 2014 (株) リナアンドルナインターナショナル, 小山市、日本 P a g e | 74 Table 2. Mean wind speed and wind speed standard deviation Month Mean Wind Speed (m/s) Standard Deviation σ (m/s) Jan. 3.61 1.17 Feb. 4.11 1.31 Mar. 4.23 1.18 April 3.79 1.33 May 3.77 1.28 June 4.00 1.41 July 3.69 1.39 Aug. 3.24 1.44 Sept. 3.20 1.51 Oct. 3.14 1.20 Nov. 3.50 1.16 Dec. 3.48 1.09 Avg. 3.65 1.28 Figure 1. Frequency distribution of measured monthly wind speed 0 5 10 15 20 25 30 35 40 45 50 0 1 2 3 4 5 6 7 8 9 Jan. Feb. Mar. April May June July Aug. Sept. Oct. Nov. Dec. Mean Wind Speed (m/s) F re q u e n cy ( % ) Asian Journal of Natural & Applied Sciences Vol. 3(1) March 2014 ________ ____________ ____________ ____________ _____ ____________ ____________ ____________ ____________ ___________ ____________ ____________ ____________ _____ ____________ ____________ ____________ ___________ ____________ ____________ ____________ ___________ ____ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ ___________ ______ ______ ______ ______ ______ ______ ______ ___________ ____________ ____________ ____________ _____ ______ ___ Copyright © 2014 Leena and Luna International, Oyama, Japan. 75 | P a g e (株) リナアンドルナインターナショナル, 小山市、日本. ISSN: 2186-8476, ISSN: 2186-8468 Print www.ajsc. leena-luna.co.jp Methods to Estimate Weibull Parameters The daily, monthly, seasonal, and yearly wind speed probability density distributions are modeled using the Weibull PDF. The Weibull PDF can be used with acceptable accuracy for prediction of wind energy output required for preliminary design and assessment of wind power plants [10]. The variation in wind speed are most often described by the Weibull PDF with two parameters, the dimensionless Weibull shape parameter , and the Weibull scale parameter which have reference values in the units of wind speed. The PDF function is given by the following [4][11][14-15] : = ⁄ . ⁄ . exp − ⁄ .......... 1 Where: = probability of observing wind speed = wind speed [ ⁄ ] = Weibull scale parameter [ ⁄ ] = Weibull shape parameter The corresponding cumulative distribution function is given by: = 1 − exp − ⁄ .......... 2 To estimate the dimensionless shape , and the scale , parameters of the Weibull distribution function, five methods have been computed. Graphical Method The graphical method (GM) is achieved through the cumulative distribution function. In this distribution method, the wind speed data are interpolated by a straight line, using the concept of least squares regression [6, 14-15]. The logarithmic transformation is the foundation of this method. By converting the equation (2) into logarithmic form, the following equation is obtained: ln [− ln 1 − ] = − .......... 3 The Weibull shape and scale parameters are estimated by plotting against ln [− ln 1 − ] in which a straight line is determined. In order to generate the line of best fit, observations of calms should be omitted from the data. The Weibull shape parameter, , is the slope of the line and the y-intercept is the value of the term − . Maximum Likelihood method The Maximum Likelihood Estimation method (MLE) is a mathematical expression known as a likelihood function of the wind speed data in time series format. The MLE method was used by Deligiorgiet al [8] and Costa Rocha et al[6], quoting Stevens and Smulders [11] in their study for the estimation of parameters of the Weibull wind spe