Machine Learning Framework for Predictive Maintenance of Distribution Transformers in Resource-Constrained Power Systems - Koffa, D. J.; Oyakhilome, S. O. (ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 2025)
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.