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文章摘要
基于核主成分分析和AP聚类算法的电力系统态势感知技术
Power system situation awareness technology based on kernel principal component analysis and AP clustering algorithm
Received:December 19, 2019  Revised:December 20, 2019
DOI:10.19753/j.issn1001-1390.2022.01.004
中文关键词: 关键词: 电力系统  相干性检测  态势感知  广域测量系统  高可再生能源渗透  核主成分分析  AP聚类
英文关键词: 
基金项目:
Author NameAffiliationE-mail
wangyansong* State Grid Beijing Electric Power Company h1546786@163.com 
1 1 1@qq.com 
2 2 2@qq.com 
3 3 1@qq.com 
4 4 2@qq.com 
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中文摘要:
      摘要: 随着可再生能源渗透水平的不断提高,现代电力系统面临着更多不可避免的不确定性,这些不确定性可能导致系统的弱阻尼振荡问题。对于可再生能源渗透率很高的电力系统,检测同步发电机之间的相干性是态势感知的关键步骤之一。本文提出了一种基于广域测量系统(Based area measurement system,WAMS)的相干检测算法,该方法采用了核主成分分析(kernel principal component analysis,KPCA)和affinity propagation(AP)聚类分析法,可应用于可再生能源广泛渗透的电力系统。本文首先提出了几种轨迹相似度指标,用于确定惯性中心(center of Inertia,COI)坐标中任意两个发电机轨迹之间的相似性,其次,提出了一种基于KPCA方法的集成轨迹相似度指标,以解决多个指标之间的相干性问题。随后采用AP聚类分析方法检测同步发电机之间的相干性,可无需预先指定聚类的数量。最后,利用高可再生能源发电渗透率的华南电力系统和包括张北风电场的华北电力系统的一部分进行仿真分析,结果证明了所提方法的适用性和实用性。
英文摘要:
      
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