Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation

Static methods which are based on statistical techniques to estimate the mean power output of a WECS (wind energy conversion system) have been widely employed in the scientific literature related to wind energy. In the static method which we use in this paper, for a given wind regime probability distribution function and a known WECS power curve, the mean power output of a WECS is obtained by resolving the integral, usually using numerical evaluation techniques, of the product of these two functions. In this paper an analysis is made of the influence of the level of fit between an empirical probability density function of a sample of wind speeds and the probability density function of the adjusted theoretical model on the relative error c made in the estimation of the mean annual power output of a WECS. The mean power output calculated through the use of a quasi-dynamic or chronological method, that is to say using time-series of wind speed data and the power versus wind speed characteristic of the wind turbine, serves as the reference. The suitability of the distributions is judged from the adjusted R-a(2) statistic (R-a(2)). Hourly mean wind speeds recorded at 16 weather stations located in the Canarian Archipelago, a an extensive catalogue of wind-speed probability models and two wind turbines of 330 and 800 kW rated power are used in this paper. Among the general conclusions obtained, the following can be pointed out: (a) that the R-a(2) statistic might be useful as an initial gross indicator of the relative error made in the mean annual power output estimation of a WECS when a probabilistic method is employed: (b) the relative errors tend to decrease, in accordance with a trend line defined by a second-order polynomial, as R-a(2) increases. (C) 2008 Elsevier Ltd. All rights reserved.