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

Huang, Xiaoqi; Zheng, Huizhe; Zeng, Chongli; Huang, Chaokai; Chen, Jianxi; Zhang, Xiaoshun

doi:10.3390/pr13092686

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Open AccessArticle

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

by

Xiaoqi Huang

¹,

Huizhe Zheng

¹,

Chongli Zeng

¹,

Chaokai Huang

¹,

Jianxi Chen

¹ and

Xiaoshun Zhang

^2,*

¹

Shantou Power Supply Bureau, Shantou 515041, China

²

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

^*

Author to whom correspondence should be addressed.

Processes 2025, 13(9), 2686; https://doi.org/10.3390/pr13092686 (registering DOI)

Submission received: 13 July 2025 / Revised: 12 August 2025 / Accepted: 22 August 2025 / Published: 23 August 2025

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry—2nd Edition)

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Abstract

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.

Keywords: new power system; electric energy meter; fault diagnosis; LightGBM

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MDPI and ACS Style

Huang, X.; Zheng, H.; Zeng, C.; Huang, C.; Chen, J.; Zhang, X. Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm. Processes 2025, 13, 2686. https://doi.org/10.3390/pr13092686

AMA Style

Huang X, Zheng H, Zeng C, Huang C, Chen J, Zhang X. Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm. Processes. 2025; 13(9):2686. https://doi.org/10.3390/pr13092686

Chicago/Turabian Style

Huang, Xiaoqi, Huizhe Zheng, Chongli Zeng, Chaokai Huang, Jianxi Chen, and Xiaoshun Zhang. 2025. "Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm" Processes 13, no. 9: 2686. https://doi.org/10.3390/pr13092686

APA Style

Huang, X., Zheng, H., Zeng, C., Huang, C., Chen, J., & Zhang, X. (2025). Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm. Processes, 13(9), 2686. https://doi.org/10.3390/pr13092686

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

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