Machine Learning Framework for Predictive Maintenance of Distribution Transformers in Resource-Constrained Power Systems

Abstract

Aging power infrastructure in developing nations faces challenges from increasing demand, renewable integration, and limited replacement capital. Nigeria's grid exemplifies these constraints, with 70% of transformers exceeding design lifetime and 15% annual failure rates causing substantial losses. Traditional time-based maintenance proves unsustainable, while existing predictive frameworks require unaffordable infrastructure. This study developed a lightweight machine learning framework for transformer failure prediction using standard Supervisory Control and Data Acquisition data. Four algorithms- Random Forest, Gradient Boosting, Support Vector Machine, and Long Short-Term Memory networks were compared using 60 months of data from 1247 transformers. Physics-informed feature engineering extracted degradation patterns from voltage, current, temperature, and load measurements. Random Forest achieved optimal performance with 94.7% accuracy, 92.3% precision, and 91.8% recall for 30 to 90 day predictions, representing 62% improvement over threshold methods. The framework identified 87% of critical failures while reducing false alarms by 64%. Economic analysis demonstrates a 260%t return on investment, an 8.3-month payback, and 8.5 billion-naira annual savings. This research proves sophisticated predictive maintenance achieved excellent results in resource-constrained environments without massive investment, offering replicable solutions for developing utilities.