Abstract
The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67-698.65W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 83.67-1021.4W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.
Highlights
Due to an increase in the pollution, rising environmental issues and continuous decrease in the reservoirs of conventional energy sources worldwide, power generation companies are moving towards renewable energy resources
Wind speed carrying maximum energy VmaxE helps in estimating the design of wind turbine whereas the most probable wind speed Vmp represents the peak of the probability density function (PDF)
The study conducted by [31] has just considered the empirical method for parameter estimation of Weibull and Rayleigh distributions, while [25] has compared the empirical method, maximum likelihood method, modified maximum likelihood method, energy factor method and graphical method for parameter assessment of Weibull distribution for Hawky’s Bay, Pakistan. [7] has compared the results of Empirical Method of Justus (EMJ), Energy Pattern Factor Method (EPFM), Maximum Likelihood Estimation Method (MLM) with the results of optimization techniques for parameter selection
Summary
Due to an increase in the pollution, rising environmental issues and continuous decrease in the reservoirs of conventional energy sources worldwide, power generation companies are moving towards renewable energy resources. Mention above it can be concluded that analysis of wind speed characteristics, selection of distribution function as assessment of wind power potential are the three most important attributes in analysis process at any particular site. These attributes are helpful for selection of suitable wind turbines, economical evaluation, auditing cost effectiveness and estimating future income of wind energy projects. If we have a look at the total increase in the installed capacity of wind energy from 2010 to 2017, with the significant addition of 150,977 MW, China is leading the world in wind power development, followed by United States, Germany, India and United Kingdom.
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