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

作　　者：杨茂[1] 王达[1] 王小海[2] 范馥麟 高博 王勃 YANG Mao;WANG Da;WANG Xiaohai;FAN Fulin;GAO Bo;WANG Bo(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education,Northeast Electric Power University,Jilin 132012,China;Inner Mongolian Electric Power Group Co.,Ltd.,Hohhot 010040,China;Institute for Energy and Environment,University of Strathclyde,Glasgow G11XW,England;State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems,China Electric Power Research Institute,Beijing 100192,China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林132012 [2]内蒙古电力(集团)有限责任公司,呼和浩特010040 [3]思克莱德大学能源与环境学院,英国格拉斯哥G11XW [4]中国电力科学研究院有限公司新能源与储能运行控制全国重点实验室,北京100192

出　　处：《高电压技术》2024年第11期5132-5141,共10页High Voltage Engineering

基　　金：国家重点研发计划(2018YFB0904200);内蒙古电力(集团)有限责任公司科技项目(DUKZZZ-YBHT-2021-JSC0401-0015)。

摘　　要：为提升超短期风电功率预测精度,提出一种数据-物理混合驱动的超短期风电功率预测方法。首先,构建一种融合双向门控循环单元的残差网络结构,将其在测试集的预测结果作为预测模板。然后,根据风速-风电转换特性,基于多项式-线性回归模型拟合风电场风速-功率曲线,在风速高波动时点,以物理机理透明的风速-功率曲线进行预测。最后,根据风速波动阈值建立不同模型之间的动态切换机制,按切换的时点修改模板预测值,对于修正风速小于切入风速的时点,将预测值置零。在吉林省某装机容量为400.5 MW的风电场提供的数据上进行仿真实验得到,测试集第16步预测的平均归一化均方根误差为0.1589,全部切换中有利切换占比达到90.86%,验证了提出的超短期风电功率预测模型的有效性和适用性。To improve the accuracy of ultrashort-term wind power prediction,a data-physical hybrid-driven ultra-shortterm 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 polynomial-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 0.1589,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 

分 类 号：TM614[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]