Enhanced Sample-Based Online Fault Identification for Electric Energy Meter Verification Devices

Lin Cong; Zhu Ge; Zhao Jing; Zhao-Lei He; He Ao

doi:10.46604/ijeti.2025.14869

Authors

Lin Cong Metering Center, Yunnan Power Grid Co., Ltd., Yunnan, China/ Key Laboratory of Green Energy and Digital Electric Power Measurement, Control and Protection of Yunnan, Yunnan, China
Zhu Ge Metering Center, Yunnan Power Grid Co., Ltd., Yunnan, China/ Key Laboratory of Green Energy and Digital Electric Power Measurement, Control and Protection of Yunnan, Yunnan, China
Zhao Jing Metering Center, Yunnan Power Grid Co., Ltd., Yunnan, China/ Key Laboratory of Green Energy and Digital Electric Power Measurement, Control and Protection of Yunnan, Yunnan, China
Zhao-Lei He Metering Center, Yunnan Power Grid Co., Ltd., Yunnan, China/ Key Laboratory of Green Energy and Digital Electric Power Measurement, Control and Protection of Yunnan, Yunnan, China
He Ao Metering Center, Yunnan Power Grid Co., Ltd., Yunnan, China/ Key Laboratory of Green Energy and Digital Electric Power Measurement, Control and Protection of Yunnan, Yunnan, China

DOI:

https://doi.org/10.46604/ijeti.2025.14869

Keywords:

automatic verification device (AVD), installed standard electric meter (ISEM), online fault identification, Monte Carlo sample enhancement, multi-class support vector machine (MSVM)

Abstract

To solve the issues of low accuracy and difficulty of online fault identification for Automatic Verification Devices (AVDs) of Electric Energy Meters (EEMs), a method based on Installed Standard Energy Meters (ISEMs) is proposed. ISEMs are tested concurrently with EEMs undergoing verification, and test data from meter positions are collected online without disrupting AVD operation. The features of the meter positions are constructed, and their principal components are extracted to reduce feature dimensionality. Unlabeled samples are categorized into typical fault categories using the K-means clustering algorithm. A Multi-Class Support Vector Machine model is trained and optimized by Bayesian optimization based on the labeled samples. The model is then employed for AVD online fault identification. Enhanced with Monte Carlo samples augmentation, the proposed approach achieves a 0.35% error rate, a 94.40% accuracy improvement compared to the model without sample enhancement. This method provides a reliable and cost-effective solution for online fault identification of AVDs.

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