Probability modeling on multiple time scales of wind power based on wind speed data

With the integration of wind power increasing, the impact of wind power fluctuation on the power system is becoming larger. However wind is a random, fluctuant and intermittent energy source. And the power output of wind turbine fluctuates with the variation of wind speed. The research of the probability distribution of wind speed is therefore very important and necessary. A number of studies have been carried out on fitting the probability distribution function of wind speed. Weibull distribution is by far the most adopted one among them. However, the traditional two-parameter Weibull distribution is difficult to approximate accurately some wind regimes in a short term or to reflect the characteristics related to time scales. In order to overcome the problem, this paper presents a new method for wind speed modeling of multiple time scales based on Weibull distribution. The maximum likelihood method is employed to estimate the parameters of Weibull distribution on multiple time scales, due to its simple and efficient characteristic. And the improved fuzzy c-means cluster method is adopted to classify these parameters, by which the parameters can be clustered into subclasses seasonally or hourly. Extensive numerical tests have been performed by MATLAB. Test results show that the model is rational and practical. The models on multiple time scales give a more detailed description of the characteristics of wind speed than the traditional Weibull distribution. The models decompose single distribution of a year into short terms and figure out seasonal rhythms and diurnal patterns of wind speed. Moreover, these models can be used in system planning or operation under the typical operating modes of practical power system.