Development of a Low-Voltage Fault Detection Model for Metering Out Fit via Unsupervised Learning-Based Labeling of AMI Data

Metering Out Fit (MOF), though essential for accurate energy billing, is customer-owned and often poorly maintained, raising the risk of undetected low-voltage faults that undermine metering reliability. This study proposes an integrated AI framework to detect such latent anomalies using large-scale data from advanced metering infrastructure. To overcome the scarcity of labeled fault data, an unsupervised phase first identified statistical outliers and established a diagnostic baseline, used to build a refined labeled dataset for training supervised ensemble classifiers, including Random Forest, LightGBM, and XGBoost. XGBoost achieved the most robust performance, balancing precision and recall. Field validation on selected cases showed that predicted voltage transformation ratio errors were consistent with actual physical degradation, suggesting the framework's potential as a scalable, automated solution for asset management, though large-scale field validation remains for future work.