OICC Press International Journal of Energy and Environmental Engineering 2251-6832 3 1 (December 2012) 2012 10 05 Mixture probability distribution functions to model wind speed distributions 10.1186/2251-6832-3-27 EN Ravindra Kollu Department of Electrical and Electronics Engineering, J.N.T. University Kakinada, Kakinada, Andhra Pradesh, 533003, IN Srinivasa Rao Rayapudi Department of Electrical and Electronics Engineering, J.N.T. University Kakinada, Kakinada, Andhra Pradesh, 533003, IN SVL Narasimham Computer Science and Engineering Department, School of Information Technology, J.N.T. University Hyderabad, Hyderabad, Andhra Pradesh, 500085, IN Krishna Mohan Pakkurthi Department of Electrical and Electronics Engineering, J.N.T. University Kakinada, Kakinada, Andhra Pradesh, 533003, IN Journal Article 2012 10 05 Abstract Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. The quality of wind speed assessment depends on the capability of chosen probability density function (PDF) to describe the measured wind speed frequency distribution. The objective of this study is to describe (model) wind speed characteristics using three mixture probability density functions Weibull-extreme value distribution (GEV), Weibull-lognormal, and GEV-lognormal which were not tried before. Statistical parameters such as maximum error in the Kolmogorov-Smirnov test, root mean square error, Chi-square error, coefficient of determination, and power density error are considered as judgment criteria to assess the fitness of the probability density functions. Results indicate that Weibull-GEV PDF is able to describe unimodal as well as bimodal wind distributions accurately whereas GEV-lognormal PDF is able to describe familiar bell-shaped unimodal distribution well. Results show that mixture probability functions are better alternatives to conventional Weibull, two-component mixture Weibull, gamma, and lognormal PDFs to describe wind speed characteristics.