中国电力 ›› 2013, Vol. 46 ›› Issue (6): 75-79.DOI: 10.11930/j.issn.1004-9649.2013.6.75.4

• 新能源 • 上一篇    下一篇

基于符号时间序列法的风电功率波动分析与预测

南晓强1, 李群湛1, 邱大强2   

  1. 1. 西南交通大学 电气工程学院,四川 成都 610031;
    2. 四川省电力超特高压运行检修公司,四川 成都 610031
  • 收稿日期:2013-01-04 出版日期:2013-06-05 发布日期:2015-12-15
  • 作者简介:南晓强(1985—),男,山西灵石人,博士研究生,从事电力系统稳定性、新能源发电及并网技术研究。E-mail: nanxiaoqiang6@163.com

Analysis and Forecast of Wind Power Fluctuation Based on Symbolized Time Series Theory

NAN Xiao-qiang1, LI Qun-zhan1, QIU Da-qiang2   

  1. 1. Electrical Engineering of School, Southwest Jiaotong University, Chengdu 610031, China;
    2. Sichuan EHV/UHV Operation and Maintenance Company, Chengdu 610031, China
  • Received:2013-01-04 Online:2013-06-05 Published:2015-12-15

摘要: 风电功率波动与预测是风电并网研究的主要内容。针对风电功率的随机波动特性,将符号时间序列方法应用于风电功率波动与预测分析中,并提出一种自适应分区方法,该方法根据数据序列分布的密集程度,实现数据序列区域的非均匀分割,找出信息量丰富的区域,以便突出反映数据的变化情况。之后,以符号序列直方图理论为基础,通过直方图求逆实现原始数据序列关键数据区域的定位,进而完成风电功率的预测。以某一风电场实测风电功率数据验证所提方法的有效性,为风电功率调度提供参考。

关键词: 风电功率波动, 风电功率预测, 符号时间序列, 自适应分区, 概率分析

Abstract: Wind power fluctuation analysis and forecasting are one of the major research topics in wind power integration. Considering the randomness of wind power fluctuation, the symbolized time series theory is applied in forecasting analysis, and an adaptive partition method is proposed to realize the non-uniform segmentation of data sequence according to its distribution intensity, by which a region containing more information can be discovered to show the variation of the data. Furthermore, the location of the key data in the original time series can be caught by inversion processing of symbol sequence histograms, leading to the results of wind power prediction. The validity of the method is verified on a wind farm, which can provide reference for wind power dispatching.

Key words: wind power fluctuation, wind power forecasting, symbolized time series theory, adaptive partition method, proportion probability

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