ALBERT

Description: With rapid increasing of wind power generating capacity, the influence of wind power on power system planning and operation becomes more and more severe. Wind power time series modeling is the basis of evaluating the influence accurately of wind power on power system, which is very important to annual wind power dispatch planning and installed capacity optimization. In this paper, a novel modeling method of wind power time series based on the definition and identification of the fluctuation process is proposed. The Gaussian function is proved to be a good choice to sketch the fluctuation process and the visualization self-organizing map clustering algorithm is used to classify the fluctuation process. The expected wind power time series is achieved by studying the statistical feature of the sample data and by analyzing the transfer characteristic of the fluctuation processes. The comparative analysis on the statistical feature of the generated time series and the sample data is presented. In terms of the 15 min and 1 h variation, autocorrelation function, monthly mean and hourly mean of the generated wind power, the proposed method in this paper are better than the commonly used Markov Chain Monte Carlo method. The proposed method can keep the variation and process characteristic of the wind power, which is very important for the study of stochastic planning and operation of the wind power in power system.