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Neural Network Based Power Meter Wiring Fault Recognition of Smart Grids Under Abnormal Reactive Power Compensation Scenarios

Author

Listed:
  • Huizhe Zheng

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Zhongshuo Lin

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Huan Lin

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Chaokai Huang

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Xiaoqi Huang

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Suna Ji

    (Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou 515041, China)

  • Xiaoshun Zhang

    (Foshan Graduate School of Innovation, Northeastern University, Foshan 528311, China)

Abstract

This paper explores the challenges of detecting wiring anomalies in three-phase, four-wire energy metering devices, especially when large amounts of reactive power compensation are involved. Traditional methods, such as the hexagon phasor diagram technique, perform well under standard loads, but struggle to adapt to new situations, such as over- or under-compensation. To overcome these limitations, this paper proposes a hybrid approach that combines mechanism-based knowledge with data-driven technologies, including backpropagation neural networks (BPNNs). This method improves the accuracy and efficiency of anomaly detection and can better adapt to a dynamic power environment. The result is improved universality of anomaly detection, which helps to achieve safer, more accurate, and more efficient smart grid operation in complex situations.

Suggested Citation

  • Huizhe Zheng & Zhongshuo Lin & Huan Lin & Chaokai Huang & Xiaoqi Huang & Suna Ji & Xiaoshun Zhang, 2025. "Neural Network Based Power Meter Wiring Fault Recognition of Smart Grids Under Abnormal Reactive Power Compensation Scenarios," Energies, MDPI, vol. 18(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:545-:d:1576323
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    References listed on IDEAS

    as
    1. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    2. Yixiao Fang & Junjie Yang & Wei Jiang, 2023. "Optimal Scheduling Strategy of Microgrid Based on Reactive Power Compensation of Electric Vehicles," Energies, MDPI, vol. 16(22), pages 1-23, November.
    3. Agata Bielecka, 2024. "Advanced Control Algorithm for Shunt Active Power Filter: Enhancing Power Quality in Autonomous Grids," Energies, MDPI, vol. 17(23), pages 1-18, December.
    4. Tao Yan & Teng Li & Zerong Liang, 2024. "Research on the Weak-Bus Voltage Support Effect of Energy Storage Type Intelligent Soft Open Point," Energies, MDPI, vol. 17(23), pages 1-13, November.
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