Partial Correlation Analysis Based Identification of Distribution Network Topology

作     者：Yanli Liu Peng Wang 

作者机构：School of electrical and information engineeringTianjin UniversityTianjin300072China 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2023年第9卷第4期

页      面：1493-1504页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Program of China(2020YFB0905900) science and technology project of SGCC(State Grid Corporation of China)(SGTJDKOODWJS 2100223)。 

主　　题：Data-driven linear correlation partial correlation smart meter topology identification. 

摘      要：Accurately identifying distribution network topol-ogy,which tends to be a mesh configuration with increasing penetration rate of distributed energy resources(DERs),is critical for reliable operation of a smart distribution network.Multicollinearity among node voltages makes existing topology identification methods unstable and inaccurate.Considering partial correlation analysis can reveal the intrinsic correlation of two variables by eliminating the influence of other variables,this paper develops a novel data-driven method based on partial correlation analysis to identify distribution network topology(radial,mesh,or including DERs)using only historical voltage amplitude data.First,maximum spanning tree of network is generated through Prim algorithm.Then,the loops of network are identified by taking tree neighbors as controlling variables in partial correlation analysis.Finally,a new topology verification mechanism based on partial correlation analysis is developed to correct wrong connections caused by multicollinearity.Test results on IEEE 33-node system,IEEE 123-node system and practical distribution network demonstrate that our method outperforms common data-driven methods,and can robustly identify both radial and mesh distribution network with DERs.IndexTerms-Data-driven,linear correlation,partial correlation,smart meter,topology identification.