Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Abstract

In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective.