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

Huizhe Zheng, Zhongshuo Lin, Huan Lin, Chaokai Huang, Xiaoqi Huang, Suna Ji and Xiaoshun Zhang ()
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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

Energies, 2025, vol. 18, issue 3, 1-21

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.

Keywords: smart grids; abnormal wiring; reactive power compensation; network models; data-driven anomaly detection (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: 2025
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