A Hybrid Approach for Low-Voltage AC Series Arc Fault Detection

Yu-Jen Liu (), Cheng-I Chen, Wei-Chung Fu, Yih- Der Lee, Chin-Chan Cheng and Yen-Fu Chen
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Yu-Jen Liu: Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 621301, Taiwan
Cheng-I Chen: Department of Electrical Engineering, National Central University, Taoyuan 320317, Taiwan
Wei-Chung Fu: Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 621301, Taiwan
Yih- Der Lee: Nuclear Instrumentation Division, Institute of Nuclear Energy Research, Taoyuan 325207, Taiwan
Chin-Chan Cheng: Nuclear Instrumentation Division, Institute of Nuclear Energy Research, Taoyuan 325207, Taiwan
Yen-Fu Chen: Nuclear Instrumentation Division, Institute of Nuclear Energy Research, Taoyuan 325207, Taiwan

Energies, 2023, vol. 16, issue 3, 1-22

Abstract: In a low-voltage electric distribution network, arc fault presents a high energy density electricity-discharging phenomenon between conductors, which is often caused by aging of electric facilities, loose contacts and terminals, or insulation failure due to internal and external destructions. A large amount of heat may be created during this discharging, which will further cause the risk of fire hazards to mitigate in the residential environment. Currently, many utility grid operators and electricity users are still devoted to seeking effective detection technology for arc fault protection. This paper proposes a hybrid approach that combines discrete wavelet transform (DWT), empirical mode decomposition (EMD), and dynamic time warping (DTW) methods for low-voltage AC series arc fault detection. In DWT, it uses time–frequency domain characteristics of the arc current signal to extract the occurrence of arc fault. In EMD, it decomposes the complex arc fault current signal into a finite intrinsic mode (IMF) signal; then, instantaneous amplitude of IMF signal is obtained by Hilbert–Huang transform (HHT) as a feature for arc fault identification. Firstly, the results of arc fault detections depend on the results from DWT and EMD. When both two methods detect different results, DTW method will be activated, using the similarity measurements between normal and arc fault current waveforms as an assistant measure to determine the occurrence of arc fault. The performance of the proposed approach is tested and validated using various electric appliances, and the results show that the proposed approach can effectively detect low-voltage AC series arc fault.

Keywords: series arc fault; arc fault detection; discrete wavelet transform; empirical mode decomposition; dynamic time warping (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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