Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm

This study proposes an intelligent method for identifying wiring errors in three-phase three-wire electricity meters using a gradient boosting machine (LightGBM) under complex load conditions, including light load and overcompensation. The work addresses a gap where intelligent fault-detection techniques have rarely been applied to three-phase three-wire wiring errors specifically under these conditions, and contributes a mechanism-informed data generation strategy tied to phase-angle behavior that can cause misidentification. Data generation and model training/evaluation were implemented in Python using LightGBM. The experiments demonstrated faster convergence (a 92.4% reduction in loss by the 50th round) and sub-2-s training time for 300 rounds, with >80% overall accuracy and 100% accuracy in specific normal-wiring scenarios relevant to misidentification risk. Feature-importance analysis identified total reactive power as the most informative input (19.8%) and confirmed the consistency between mechanism and model behavior. These results suggest a practical path to automated and accurate wiring-error detection in modern power systems with significant load variability.