Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features

During ac series arc faults (SAFs), arcing current features can change significantly or vanish rapidly under different load-combination modes and fault inception points. The phenomena make it very challenging for feature-extracting algorithms to detect SAFs. To address the issues, this article presents a detection model based on regular coupling features (RCFs). After the model is only trained by the samples in single-load circuits, it can detect SAFs under unknown multiload circuits. To extract the RCFs, asymmetric magnetic flux is coupled by passing the live line and the neutral line through the current transformer. The coupling signals are not influenced by the multiload circuits. According to the unique signals, two time-domain features and one frequency-domain feature are extracted to represent the RCFs, including impulse-factor analysis, covariance-matrix analysis, and multiple frequency-band analysis. Then, the impulse factor and its threshold are used to preprocess the signals and decrease analysis complexity for the classifier. Finally, the experimental results show that the proposed method has significantly improved generalization ability and detection accuracy in SAF detection.