风电场短期风速的多变量局域预测法  

Multivariate Local Prediction Method for Short-term Wind Speed of Wind Farm

作　　者：郭创新[1] 王扬[1] 沈勇[2] 王媚[3] 曹一家[4] 

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027 [2]上海市电力公司奉贤供电公司,上海市奉贤区201400 [3]上海市电力公司检修公司,上海市闸北区200072 [4]湖南大学电气与信息工程学院,湖南省长沙市410082

出　　处：《中国电机工程学报》2012年第1期24-31,22,共8页Proceedings of the CSEE

基　　金：新世纪优秀人才支持计划项目(NCET-07-0745);浙江省自然科学基金项目(R107062);国家863高技术基金项目(2008AA05Z210);高等学校博士学科点专项科研基金资助项目(20090101110058)~~

摘　　要：风电场短期风速的统计预测方法大都基于单变量风速时间序列,预测精度有限,而在多变量预测中选取哪些变量又没有明确的方法。针对此问题,提出一种风电场短期风速的多变量局域预测法,该方法基于相关性原则来筛选多变量时间序列数据并构造多变量相空间,在该相空间中寻找预测状态点的邻域点并建立支持向量回归(support vectorregression,SVR)模型。采用风电场实测数据进行验证,结果表明:在构造相空间时,增加彼此相关程度低的变量数目,能够明显提升局域法的搜索能力,找到与预测点相似程度更高的邻域点并将其用于模型训练;同时结合SVR模型的高维非线性拟合能力,有效地提高了短期风速预测精度。Most statistics prediction methods for short-term wind speed are based on univariate wind speed time series, they have limited prediction accuracy; however there is no specific method for selecting variables of multivariate prediction. This paper presented a multivariate local predictor for short-term wind speed prediction of wind farm. It sifted multivariate time series by correlation principle to reconstruct multivariate phase space, and searched the neighborhood of the prediction state points to build the support vector regression models. The data of real-world collected from a wind farm was applied to verify the conclusions. The example results show that the proposed method can improve the searching efficiency of local predictor that can find much more similar neighbor points. And combining with support vector regression （SVR） model that could provide good capability of nonlinear fitness, it can effectively improve the accuracy of short-term wind speed prediction.

关 键 词：风速预测 局域预测 相关系数 支持向量回归 相空间重构 

分 类 号：TM71[电气工程—电力系统及自动化]