电力系统自动化2017，Vol.41Issue(17)：29-36,59,9.DOI:10.7500/AEPS20161201016
变尺度时间窗口和波动特征提取的短期风电功率组合预测
Combined Prediction for Short-term Wind Power Based on Variable Time Window and Feature Extraction
摘要
Abstract
Short-term wind power prediction is a crucial technology to ensure security and stable operation of power grid connected with large-scale wind farms.The fluctuation and predication accuracy of wind power are definitely affected by the intermittence and variability of wind speed.This paper proposes a combined prediction method for short-term wind pwer based on the variable time window and feature extraction in wind speed.At first, a multifractal spectrum is used to investigate wind speed characterizations.Then, on the basis of the wind fluctuation definition, an abstracting feature extraction approach is proposed by use of a sliding variable time window algorithm capable of self-adaptively adjusting the size of time window width.The historical data is classified according to the fluctuation events abstracting results.Different prediction models are developed by selecting specific parameters after analyzing the fluctuation events characteristics.The presented method employs spectrum analysis to correct errors in power prediction full aware of the complexity and multiformity of output wind power in different time periods.Finally, case studies are carried out to verify and evaluate the availability of the proposed model.Results show that the short-term forecasting accuracy of wind power has been improved in various wind situations.关键词
风电功率预测/特征提取/变尺度时间窗口/组合预测Key words
wind power prediction/feature extraction/variable time window/combined prediction引用本文复制引用
叶林,滕景竹,蓝海波,仲悟之,吴林林,刘辉,王铮..变尺度时间窗口和波动特征提取的短期风电功率组合预测[J].电力系统自动化,2017,41(17):29-36,59,9.基金项目
国家自然科学基金资助项目(51477174) （51477174）
国家自然科学基金中英国际合作交流基金资助项目(51711530227) （51711530227）
国家电网公司科技项目(5201011600TS).This work is supported by National Natural Science Foundation of China (No.51477174, No.51711530227) and State Grid Corporation of China (No.5201011600TS). （5201011600TS）
