A Low voltage AC fault arc detection method based on multi-feature fusion

Abstract

The complex electrical environment makes it difficult for most arc detectors on the market to accurately detect faulty arcs. A universal fault arc detection method based on machine learning (ML) algorithms is proposed in this paper. To address the potential hazards of fault arcs in the power system, firstly, the causes, time-domain, and frequency-domain characteristics of fault arcs were analyzed. Then, an arc experiment platform was built according to national standards, and current data was collected under various load conditions. Finally, the arc detection model was trained based on ML. The main contribution of this study is to explore a feature combination that is not affected by load type interference and has strong generalization ability for identifying fault arcs, clarify the key factors and feature extraction methods for identifying fault arcs, and develop a set of high-precision arc detection methods, providing strong guarantees for the stable operation of the power system.