Ultra-short-term wind power prediction and its application in early-warning system of power systems security and stability

With the growth of wind power penetration, the study and application of ultra-short-term wind power forecasting which is suitable for on-line early-warning system is of great importance, which could improve the grids receptiveness, security and stability. Firstly, an ultra-short-term wind power forecasting model is established based on autoregressive integrated moving average (ARIMA) time series model. Secondly, considering the requirements of on-line early-warning system, a technique of on-line computing white noise sequence is discussed. And then the way of improving the adaptability is proposed. Thirdly, the applications of the proposed forecasting method in on-line early-warning system are designed. Finally, taking Ningxia wind farms operation data as example, a comparison is taken between persistence forecasting method and the proposed method. And the validity of the proposed method is proved.