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Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems
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作者 Ye Tian Mou-Fa Guo Duan-Yu Chen 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2023年第2期599-611,共13页
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 lea... 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. 展开更多
关键词 Resonant grounding distribution systems intermittent arc faults(IAFs)detection “For IAFs Neuron Elaboration Net”(FINET) principal-subordinate factor(PSF)
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