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A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring

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  • Krzysztof Dowalla

    (Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Piotr Bilski

    (Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Robert Łukaszewski

    (Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Augustyn Wójcik

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland)

  • Ryszard Kowalik

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland)

Abstract

Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel arc detection and faulty line identification method. It can be easily used in the low-voltage Alternate Current (AC) household network for arc detection in the Non-Intrusive Load Monitoring (NILM). Unlike existing methods, the proposed approach exploits both current and voltage signal time domain analysis. Experiments have been conducted with up to six devices operating simultaneously in the same circuit with an arc fault generator based on the IEC 62606:2013 standard. Sixteen time-domain features were used to maximize the arc-fault detection accuracy for particular appliances. Performance of the random forest classifier for arc fault detection was evaluated for 28 sets of features with five different sampling rates. For the single period analysis arc, detection accuracy was 98.38%, with F-score of 0.9870, while in terms of the IEC 62606:2013 standard, it was 99.07%, with F-score of 0.9925. Location of a series arc fault (line selection) was realized by identifying devices powered by the faulty line. The line selection was based on the Mean Values of Changes feature vector ( MVC 50 ), calculated for absolute values of differences between adjacent current signal periods during the arc fault. The fault location accuracy was 93.20% for all cases and 98.20% for cases where the arc fault affected a single device.

Suggested Citation

  • Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring," Energies, MDPI, vol. 16(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:171-:d:1013319
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    References listed on IDEAS

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    1. Xinran Li & Chenyun Pan & Dongmei Luo & Yaojie Sun, 2020. "Series DC Arc Simulation of Photovoltaic System Based on Habedank Model," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Yao Wang & Cuiyan Bai & Xiaopeng Qian & Wanting Liu & Chen Zhu & Leijiao Ge, 2022. "A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System," Energies, MDPI, vol. 15(8), pages 1-20, April.
    3. Lina Wang & Ehtisham Lodhi & Pu Yang & Hongcheng Qiu & Waheed Ur Rehman & Zeeshan Lodhi & Tariku Sinshaw Tamir & M. Adil Khan, 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems," Energies, MDPI, vol. 15(10), pages 1-16, May.
    4. Michał Dołęgowski & Mirosław Szmajda, 2021. "A Novel Algorithm for Fast DC Electric Arc Detection," Energies, MDPI, vol. 14(2), pages 1-17, January.
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