Short-term Prediction of Wind Power Based on Kalman Filter Tracking Fusion

In view of the uncertainty of the measurement information of a single sensor, an ultra-short-term prediction method of wind power based on Kalman filter track fusion is proposed in this paper. Firstly, according to the characteristics of randomness and volatility of wind power, the future development trend of the data is found from the historical data by using grey theory, and the ultra-short-term predicted values of wind power based on GM(1,1) and GM(1,2) models are taken as two tracks. In order to further improve the prediction accuracy of wind power, the Kalman filter algorithm is used to carry out the track fusion of wind power obtained from the above two models. The validity of the proposed method is verified by the simulation analysis of the actual operation data of a wind farm in China. The simulation results show that the prediction accuracy of wind power obtained by the Kalman filter track fusion method is significantly higher than that obtained by single prediction method.