基于数据相关性分析的电网故障时刻确定与故障识别方法  

Fault Time Determination and Identification in Power Grids Based on Data Correlation Analysis

作　　者：刘晓军[1] 马羽中 杨冬锋 赵芷莹 白尚旻 LIU Xiaojun;MA Yuzhong;YANG Dongfeng;ZHAO Zhiying;BAI Shangmin(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University),Ministry of Education,Jilin 132012,Jilin Province,China;Jilin Power Supply Company,State Grid Jilin Electric Power Company,Jilin 132001,Jilin Province,China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林省吉林市132012 [2]国网吉林省电力有限公司吉林市供电公司,吉林省吉林市132001

出　　处：《电网技术》2022年第4期1529-1538,共10页Power System Technology

基　　金：国家重点研发计划(智能电网技术与装备重点专项(2016YFB0900600));国家电网公司科技项目(52094017000W)。

摘　　要：提出了一种基于正态分布的故障时刻确定方法和基于特征向量法的故障类型识别与故障定位方法。利用各节点处PMU采集的三相电压数据构建数据源矩阵,通过分析时间窗矩阵中各列数据的正态分布曲线,实现故障时刻的确定;再利用数据源矩阵的样本协方差矩阵作为特征向量法的判断矩阵,通过分析2个最大特征值对应特征向量中的异常元素,确定故障位置并识别故障类型。以IEEE39节点系统为例,验证了方法在电网发生各类故障以及复故障时均能有效实现故障时刻确定与故障识别,并且证明了所提方法对不良数据有较好的鲁棒性。A fault time determination based on the normal distribution and a fault identification based on the eigenvector method are proposed.The data source matrix is constructed by using the three-phase voltage data which is collected by the PMU of each node.The fault time is determined through analysing the normal distribution curve of each column data in the time window matrix.Then,the judgment matrix of eigenvector method is constructed with the sample covariance matrix.The fault type is identified by analysing the anomalous element of eigenvectors of the two largest eigenvalues.The IEEE 39 nodes system is used to verify that the method can effectively achieve the fault time determination and the fault identification when all kinds of faults and complex faults occur in the power grid.And it is also proved that the method has a certain anti-interference ability to the bad data.

关 键 词：特征向量法 正态分布 样本协方差矩阵 复故障 故障识别 

分 类 号：TM721[电气工程—电力系统及自动化]