Wind energy analysis based on maximum entropy principle (MEP)-type distribution function

This paper reports an analysis of the wind characteristics of four stations (Elazig-Maden, Elazig-Keban, Elazig, Elazig-Agin) that have been investigated over a period of 8 years (1998–2005). The probabilistic distributions of wind speed are a critical piece of information needed in the assessment of wind energy potential, which have been conventionally described by various empirical correlations. Among the empirical correlations, the Weibull distribution has been the most popular one due to its ability to fit most accurately the variety of wind speed data measured at different geographical locations in the world. This study develops a theoretical approach to the analytical determination of wind speed distributions through application of the maximum entropy principle (MEP). The statistical analysis parameter based on wind power density is used as the suitable judgment criterion for the distribution functions. It is shown that the MEP type distributions not only agree better with a variety of measured wind speed data than the conventionally used empirical Weibull distribution but also can represent the wind power density much more accurately. Therefore, the MEP type distributions are more suitable for assessment of the wind energy potential and the performance of wind energy conversion systems.