Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 70, Issue 2, Pages 2026-2036Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2022.3165260
Keywords
Circuit faults; Feature extraction; Couplings; Time-domain analysis; Voltage measurement; Magnetic flux; Discrete wavelet transforms; Ac series arc fault detection; signal regularity; time-series reconstruction based on spectral features (TSRSF); unknown multiload circuits
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This article investigates the regularity of signal characteristics in the detection of ac series arc faults (SAF). By adopting a coupling method and a time-series reconstruction method, it demonstrates the feasibility and accuracy of identifying SAFs in unknown multiload circuits.
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.
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