A high resistance grounding fault identification method for distribution network considering topological connection

Abstract Traditional high resistance grounding fault identification in distribution networks fails to meet load demands, demanding optimization for reliability and economics. A novel approach considers topological connections, applies the Hilbert-Huang transform for signal decomposition, refines components, and utilizes particle swarm optimization to enhance feature vectors. This defines an association matrix and leverages zero sequence current features, accurately pinpointing suspected high resistance ground faults through filtered second derivative concavity and zero crossing point analysis. Experimental data underscores the design method’s exceptional calculation efficiency, capping at 40 milliseconds, and maintaining over 90% fault recognition accuracy. Recognition time notably diminishes, shrinking from 0.91 seconds for 10 iterations to 0.28 seconds for 50 iterations.