风电场超短期风速预测的相空间优化邻域局域法  

An Optimal Neighborhood in Phase Space Based Local Prediction Method for Ultra-short-term Wind Speed Forecasting

作　　者：王扬[1] 张金江[2] 温柏坚[3] 郭创新[1] 曹一家[1,4] 吴栋梁[1] 

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027 [2]浙江科技学院自动化与电气工程学院,浙江省杭州市310023 [3]广东省电力调度中心,广东省广州市510600 [4]湖南大学电气与信息工程学院,湖南省长沙市410082

出　　处：《电力系统自动化》2011年第24期39-43,58,共5页Automation of Electric Power Systems

基　　金：教育部新世纪优秀人才支持计划资助项目(NCET-07-0745);浙江省自然科学基金资助项目(R107062)~~

摘　　要：基于相空间重构技术和局域预测法,提出一种风电场超短期风速预测的新方法。该方法通过优化的相空间邻域寻找预测状态点在相空间中的邻域点,并建立支持向量回归(SVR)模型。通过考察伪近邻点的比重来选取合适的邻域半径,保证了邻域点与预测状态点的高度相似性,而SVR模型则具有很强的高维非线性拟合能力。实例分析表明,该方法与其他方法相比具有较好的超短期风速预测效果。A new approach to ultra-short-term wind speed forecasting is presented.It is based on the phase space reconstruction technique and the local prediction method,to search the neighbors in the optimal neighborhood in the phase space and to build support vector regression(SVR) models.This approach finds the optimum neighborhood by considering of proportion of false neighbors,which guarantees the high similarity between neighbors and prediction state points,and the SVR model has a good capability of nonlinear fitness.The results have demonstrated the accuracy of the proposed method in comparison with that by other prediction methods.

关 键 词：风力发电 超短期风速预测 局域预测法 支持向量回归(SVR) 相空间重构 伪近邻点 

分 类 号：TM614[电气工程—电力系统及自动化] TM744