Series Arc Fault Detection Using Regular Signals and Time-Series Reconstruction

This article investigates into signal regularity in ac series arc fault (SAF) detection. The regularity can solve the vital problem that SAF current characteristics change or disappear in unknown multiload circuits. A coupling method is adopted to capture high-frequency differential signals. The coupling signals show that the waveform in single-load circuits closely resembles the waveform in multiload ones. However, the coupling method confuses normal signals with arcing ones in dimmer loads. To address the issue, this article presents a time-series reconstruction method based on spectral features. First, the spectral features are analyzed between fault-like signals and fault signals. According to the spectral features and the desirable margin, the time series is self-adaptively decomposed and reconstructed. Then, the pulse-recognition algorithm is used to extract arcing features of the reconstructed signals. Finally, the detection method determined by single-load circuits is used to identify SAFs in unknown multiload ones. The results show the presented approach has good generalization performance and identification precision under arbitrary circuits.