Analysis of transformation methods for mathematical modeling of wind resource

In the current global renewable source summit environmental policy, wind power industry has been growing six times fast in recent years. This paper describes and compares the techniques of modeling the wind speed while assessing the wind energy potential of the geographic location of the region. The probability density functions are discussed to designate the wind speed density functions. Transformation method proposed to obtain a wind power density model and its statistical properties are discussed particularly from three pdfs. The wind power density and cumulative density functions are derived using the transformation method. The parameters of those distributions are estimated using the maximum likelihood method. The quality of the goodness of fit is analyzed and compared using the Kolmogorov-Smirnov test. An application of the mathematical model is demonstrated by a case study that involves wind speed data from several stations in India. Also, the descriptive statistics such as mean, standard deviation, skewness and kurtosis of the wind speeds of the different stations are deliberated which provides better intuition about the characteristics and properties of power density. Among the discussed distribution functions, the Burr probability density function appears to be the most reliable statistical distribution for the stations taken for the analysis.