Enhanced Sample-Based Online Fault Identification for Electric Energy Meter Verification Devices - Lin Cong; Zhu Ge; Zhao Jing; Zhao-Lei He; He Ao (International Journal of Engineering and Technology Innovation, 2025)
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.