风电集群短期及超短期功率预测精度改进方法综述  

A Summary of the State of the Art for Short-term and Ultra-short-term Wind Power Prediction of Regions

作　　者：彭小圣[1] 熊磊[1] 文劲宇[1] 程时杰[1] 邓迪元 冯双磊[2] 王勃[2] 

机构地区：[1]华中科技大学电气与电子工程学院,湖北省武汉市430074 [2]中国电力科学研究院,北京市海淀区100192

出　　处：《中国电机工程学报》2016年第23期6315-6326,6596,共12页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51529701);国家电网科技项目<基于集群划分的新能源功率预测技术研究和示范>资助~~

摘　　要：风电集群短期及超短期功率预测是提升电网健壮性的有力手段。该文总结国内外风电集群短期与超短期功率预测技术的现状,从集群和单个风电场两个方面,归纳风电功率预测技术的分类;从预测流程、数据来源、数据流向、物理层次4个方面论述风电集群功率预测系统的整体框架;提出具有泛化意义的风电功率预测的物理层次结构,并从数据层、映射层、特征层、模型层、反馈层5个不同的层面讨论风电功率预测技术的精度提升方法及其发展方向,对短期、超短期风电功率预测、集群功率预测的研究具有一定参考价值。Short-term and ultra-short-term wind power prediction（WPP） in regions is an effective method to enhance the robustness of the power grid. The state of the art of short-term and ultra-short-term WPP techniques was summarized in the paper. The classification of WPP techniques was discussed, in terms of prediction method of single wind farm and regions. The overall framework of the regional WPP system was discussed in four aspects, including the forecasting flow chart, data sources, data flows and the physical levels. Generalization physical layers of wind power forecasting was presented in five levels, data layer, mapping layer, feature layer, methodology layer, and feedback layer, based on which the improvement methods for WPP were discussed. The proposed improvement methods for the five physical levels of WPP will contribute to both short-term and ultra-short-term WPP in regions.

关 键 词：风电集群预测 短期功率预测 超短期功率 预测物理层次 预测精度 

分 类 号：TM711[电气工程—电力系统及自动化]