高电压技术2024，Vol.50Issue(11)：5132-5141,10.DOI:10.13336/j.1003-6520.hve.20230401
基于数据物理混合驱动的超短期风电功率预测模型
Ultra-short Term Wind Power Prediction Method Based on Data Physics Hybrid Driven Model
摘要
Abstract
To improve the accuracy of ultrashort-term wind power prediction,a data-physical hybrid-driven ultra-short-term wind power prediction method is proposed.First,the ultrashort-term WPP model with bidirectional recurrent residual net-work is constructed,and the prediction results in the test set are used as the prediction template.Then,a pol-ynomial-linear regression model is utilized to fit the wind speed-power curve of the wind farm,and the wind-power curve(WPC)is used to predict at the high fluctuation points.Finally,a dynamic switching mechanism between different models is established according to the wind speed fluctuation threshold,the template prediction value is modified according to the switching time point,and the prediction value is set to zero for the samples that the corrected wind speed is less than the cut-in wind speed.Experimental validation is carried out with data provided by a wind farm with an installed capacity of 400.5 MW in Jilin province of China,the average normalized root mean square error predicted in step 16 of the test set is 0.158 9,and the favorable switchover accounts for 90.86%of all the switches,which verify the validity and applicability of the proposed ultra-short-term wind power prediction model.关键词
风电场/超短期预测/数据物理混合驱动/切换机制/波动阈值/深度残差网络Key words
wind farm/ultra-short-term forecasting/data-physical hybrid driven/switching mechanism/fluctuation threshold/deep residual network引用本文复制引用
杨茂,王达,王小海,范馥麟,高博,王勃..基于数据物理混合驱动的超短期风电功率预测模型[J].高电压技术,2024,50(11):5132-5141,10.基金项目
国家重点研发计划(2018YFB0904200) （2018YFB0904200）
内蒙古电力(集团)有限责任公司科技项目(DUKZZZ-YBHT-2021-JSC0401-0015).Project supported by National Key R&D Program of China(2018YFB0904200),Science and Technology Project of Inner Mongolia Electric Power(Group)Co.,Ltd.(DUKZZZ-YBHT-2021-JSC0401-0015). （集团）
