High-impedance Fault Detection Method Based on Feature Extraction and Synchronous Data Divergence Discrimination in Distribution Networks

作     者：Yang Liu Yanlei Zhao Lei Wang Chen Fang Bangpeng Xie Laixi Cui Yang Liu;Yanlei Zhao;Lei Wang;Chen Fang;Bangpeng Xie;Laixi Cui

作者机构：School of Electrical and Electronic TechnologyShandong University of TechnologyZiboChina State Grid Shanghai Electric Power Research InstituteShanghaiChina State Grid Shanghai Pudong Electric Power Supply CompanyShanghaiChina 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2023年第11卷第4期

页      面：1235-1246页

核心收录：

学科分类：0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 

基　　金：supported in part by the National Key Research and Development Program of China(No.2017YFB0902800) Science and Technology Project of the State Grid Corporation of China(No.52094017003D)。 

主　　题：High-impedance fault micro-phase measurement unit fault detection distribution network optimal placement 

摘      要：High-impedance faults(HIFs)in distribution networks may result in fires or electric shocks.However,considerable difficulties exist in HIF detection due to low-resolution measurements and the considerably weaker time-frequency characteristics.This paper presents a novel HIF detection method using synchronized current information.The method consists of two stages.In the first stage,joint key characteristics of the system are extracted with the minimal system prior knowledge to identify the global optimal micro-phase measurement unit(μPMU)placement.In the second stage,the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data inμPMUs in a high-noise environment.l2,1 principal component analysis(PCA),i.e.,PCA based on the l2,1 norm,is applied to an extracted system state and fault features derived from different resolution data in both stages.An economic observability index and HIF criteria are employed to evaluate the performance of placement method and to identify HIFs.Simulation results show that the method can reliably detect HIFs with reasonable detection accuracy in noisy environments.