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

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.