基于Informer-SDT-xLSTM协同机制的风电功率爬坡事件预测OA

Under extreme weather conditions,wind power output fluctuates drastically within a short period,mak-ing wind power ramping highly likely to occur.However,existing studies still exhibit limited accuracy and reliabil-ity in predicting wind power ramping under extreme weather.To address this issue,a wind power ramping prediction method based on an Informer-SDT-xLSTM collaborative mechanism is proposed.An Informer neural network model is first developed for wind power forecasting.The preliminary forecasting results are then subjected to ramping detec-tion using spinning door transformation(SDT)considering bump events,from which extrema within ramping seg-ments are extracted.Subsequently,an extended long short-term memory(xLSTM)network is employed to correct the extrema detected in the ramping segments,thereby improving prediction accuracy.The proposed method is vali-dated using data from a wind farm.Results demonstrate that the model can accurately detect wind power ramping un-der extreme weather conditions and effectively reduce prediction deviations associated with such events.