With the integration of large-scale wind power into power system

wind power ramping events(WPREs)have brought series of effect on the stability and safety for power system operation. It is becoming more and more urgent to study the power prediction when the ramping event occurs. Ultra-short-term prediction and correction model considering the WPREs is employed based on extreme learning machine(ELM). Firstly

the optimized swinging door algorithm is utilized to identify the WPREs and extract the feature value of WPREs. Then

the fuzzy C-means clustering model is established to obtain the congeneric data. On this basis

by using ELM to train and predict the data mentioned above

similar ramping events to the results of current wind power prediction are found from historical WPREs library by tuple vector time warp(TVTW)algorithm

which obtains historically similar ramping events of power prediction. Finally

the wind power prediction result is corrected corresponding to the difference between the historical value and the actual value of predicted power. Case studies show that the proposed method can accurately identify WPREs and significantly improve the precision of ultra-short-term forecasting for wind power.