Multi-load Series Fault Arc Identification Based on PSO-RELM

Most of the existing fault arc circuit breaker is only for single circuit single load. Aiming at the problem of complex running load of low-voltage power lines, set up an experimental platform to collect the current signals of series fault arcs in main and branch lines under the conditions of single load operation and multi-load operation of typical household loads, to create a waveform database. Various time-frequency domain feature quantities are extracted, and a subset of the feature quantity data is constructed by random forest and input to a regularized limit learning machine (RELM) for fault arc detection, and the most appropriate input weights and implied layer thresholds of the RELM are optimally selected by a particle swarm optimization (PSO) to improve the accuracy of fault arc identification. The result shows that the PSO-RELM algorithm achieves an accuracy of 98.37% and can accurately identify fault arcs in multi-load situations.