Enhancing Transformer Maintenance with Predictive Analytics: A Cost-Effective and Reliable Solution using Machine Learning

Transformer monitoring is traditionally carried out by manual inspections and cyclical assessments that have led to costly maintenance, unscheduled outages, and delayed interventions. The proposed system improves transformer maintenance using predictive analytics powered by machine learning (ML). It differs from traditional systems by anticipating breakdowns in advance using real-time data from sensors tracking multiple parameters including temperature, vibration, load, and oil quality. Maintenance savings achieved through the technique amount to 92% prediction accuracy, 88% precision, and 85% recall compared to 78%-85% accuracy and 70%-80% precision in existing systems. Annual total expenditures are reduced from $500,000-$700,000 in existing systems to $370,000. Downtime caused by maintenance is reduced from 120 hours to 40 hours annually, increasing reliability to 95%. The system shows an effective improvement in both the cost and efficiency of transformer maintenance.