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A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System

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  • Yao Wang

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300132, China)

  • Cuiyan Bai

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300132, China)

  • Xiaopeng Qian

    (Zhejiang High and Low Voltage Electric Equipment Quality Inspection Center, Yueqing 325604, China)

  • Wanting Liu

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300132, China)

  • Chen Zhu

    (State Grid Beijing Electric Power Co., Ltd., Fangshan Power Supply Branch, Beijing 102400, China)

  • Leijiao Ge

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2877-:d:793989
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    References listed on IDEAS

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    1. Lu, Shibo & Phung, B.T. & Zhang, Daming, 2018. "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 88-98.
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    Cited by:

    1. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.
    3. 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.

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