基于继电保护信息与改进谱聚类分析的电网故障区域识别算法  

Power grid fault region identification algorithm based on relay protection information and improved spectral clustering analysis

作　　者：刘建锋 张科曌 高亮 黄世龙 LIU Jianfeng;ZHANG Kezhao;GAO Liang;HUANG Shilong(Shanghai University of Electric Power, Shanghai 200090, China)

机构地区：[1]上海电力大学

出　　处：《电力系统保护与控制》2019年第14期37-43,共7页Power System Protection and Control

基　　金：上海市青年科技英才扬帆计划(16YF1404800);三峡大学梯级水电站运行与控制湖北省重点实验室基金资助项目(2015KJ11)~~

摘　　要：为了提高聚类算法在故障区域识别中的定位能力与容错性,提出了一种基于继电保护状态信息与谱聚类相结合的电网故障区域识别算法。该方法首先利用各个智能电子装置(IED)提供的继电保护状态值形成特征向量,然后根根事先划定的元件与IED的关联方式,对谱聚类结果进行分析,最后得到故障元件。在容错性方面,对特征向量畸变对聚类分析的影响做出了分析,并发现使用密度调整谱聚类可以取得更好的效果。经仿真实验表明,对核函数的密度差进行改进,相比于已有的基于聚类原理的故障定位算法有更高的定位精度和容错能力,并且定位能力有所提高。In order to improve the positioning ability and fault tolerance of clustering algorithm in fault area identification, this paper proposes a grid fault area identification algorithm based on relay protection information and spectral clustering. The method firstly uses the relay protection state value provided by each Intelligent Electronic Device(IED) to form a feature vector, and then analyzes the spectral clustering result according to the previously delineated association between the component and the IED, and finally obtains the faulty component. In terms of fault tolerance, this paper analyzes the influence of eigenvector distortion on clustering analysis and finds that using density-adjusted spectral clustering can achieve better results. The simulation experiments show that the density difference of the kernel is improved, and the fault location algorithm based on the clustering principle has higher positioning ability and fault tolerance, and the positioning capability is improved.

关 键 词：故障区域识别 谱聚类 密度差 继电保护信息 容错性 

分 类 号：TM77[电气工程—电力系统及自动化]