Multi-Risk Factor and Knowledge Entropy Framework for Alternating Current Arc Fault Detection

This study addresses the significant challenges associated with detecting series AC arc faults, particularly in the context of diverse load types, coupled features, and the superimposed characteristics of arc signals. To overcome these complexities, a novel AC arc detection methodology is proposed, which leverages the construction of multiple risk factors. Specifically, the approach introduces three innovative risk factors: the abnormal distribution risk factor, the harmonic energy risk factor, and the abnormal pulse risk factor (collectively referred to as AHA). These factors are designed to extract the distinct characteristics of AC arc faults across varying operational scenarios. Furthermore, an expert knowledge-driven fusion framework based on information entropy (KE) is developed to integrate these risk factors, enhancing the robustness and precision of the detection process. Experimental validation conducted in low-voltage electrical environments demonstrates that the proposed AHA-KE model achieves high detection accuracy, effectively addressing the inherent challenges of arc fault detection in such settings.