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

In resonant grounding systems, most single-phase-to-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 newly-designed 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.