Ultra-Short-Term Prediction of Wind Power Based on Fuzzy Clustering and RBF Neural Network

Abstract

High-precision wind power forecast can reduce the volatility and intermittency of wind power output, which is conducive to the stable operation of the power system and improves the system's effective capacity for large-scale wind power consumption. In the wind farm, the wind turbines are located in different space locations, and its output characteristics are also affected by wind direction, wake effect, and operation conditions. Based on this, two-step ultra-short-term forecast model was proposed. Firstly, fuzzy C-means clustering (FCM) theory was used to cluster the units according to the out characteristics of wind turbines. Secondly, a prediction model of RBF neural network is established for the classification clusters, respectively, and the ultra-short-term power forecast is performed for each unit. Finally, the above results are compared with the RBF single prediction model established by unclassified g wind turbines. A case study of a wind farm in northern China is carried out. The results show that the proposed method can effectively improve the prediction accuracy of wind power and prove the effectiveness of the method.