Wind power plant cluster active power prediction method and system

Wind power plant cluster active power prediction method and system

The invention provides a wind power plant cluster active power prediction method and system. The prediction method comprises: acquiring historical data of active power of a wind power plant cluster; based on an error feed-forward prediction method and a continuity method, establishing an error feed-...

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Hauptverfasser: ZENG PENG, HAO JIE, WANG WEIRU, CHENG XUETING, LI MENGZAN, MA DONGJUAN, ZHENG HUIPING, BAO YUESHUANG, LIU XINYUAN, BO LIMING, ZHANG YIFAN, YANG WEIWEI, CUI YANG, PI JUN, ZHANG YING, GAO HONG
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:The invention provides a wind power plant cluster active power prediction method and system. The prediction method comprises: acquiring historical data of active power of a wind power plant cluster; based on an error feed-forward prediction method and a continuity method, establishing an error feed-forward prediction model reflecting the wind power change trend of the wind power plant cluster; training the error feed-forward prediction model based on historical data, determining an optimal error limit value in the error feed-forward prediction model, and obtaining a trained error feed-forward prediction model; and inputting the trained error feed-forward prediction model based on historical data of the first n moments of a to-be-predicted moment to obtain an active power prediction value of the to-be-predicted moment. The influence of recent and historical wind power plant and wind power cluster power data and the regularity shown by cluster data are fully considered, the prediction precision is improved by c