Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems  

作　　者：Ye Tian Mou-Fa Guo Duan-Yu Chen 

机构地区：[1]College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China [2]Department of Electrical Engineering,Yuan Ze University,Taiwan 32003,China [3]Fuzhou Power Supply Company of State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350009,China

出　　处：《CSEE Journal of Power and Energy Systems》2023年第2期599-611,共13页中国电机工程学会电力与能源系统学报（英文）

基　　金：sponsored by the National Natural Science Foundation of China (No.51677030).

摘　　要：In resonant grounding systems,most single-phaseto-ground faults evolve from IAFs(Intermittent Arc Faults).Earlier detection of IAFs can facilitate fault avoidance.This work proposes a novel method based on machine learning for detecting IAFs in three steps.First,the feature of zero-sequence current is automatically extracted and selected by a newlydesigned FINET(“For IAFs,Neuron Elaboration Net”),instead of traditional feature selection based on time-frequency decomposition.Moreover,data of the zero-sequence current divided by different time windows are successively input into the trained FINET.A proposed PSF(principal-subordinate factor)analyses the results obtained from FINET to improve anti-interference in the mentioned IAF detection algorithm.Experiments using PSCAD/EMTDC software simulation data show the proposed method is feasible and highly adaptable.In addition,the detection result of on-site recorded data demonstrates the effectiveness of the proposed method in practical resonant grounding systems.

关 键 词：Resonant grounding distribution systems intermittent arc faults(IAFs)detection “For IAFs Neuron Elaboration Net”(FINET) principal-subordinate factor(PSF) 

分 类 号：TM862[电气工程—高电压与绝缘技术]