Development of a Digital Mirror for Power Transformers and Reactors

SCADA/online sensor streams, and periodic diagnostic tests into a unified platform. A modular hybrid analytics stack combines physics-based models (thermal hot-spot and insulation ageing, bathtub failure profiles) with data-driven AI/ML (anomaly detection, pattern recognition, predictive forecasting) to implement parallel Health, Ageing, Thermal, Failure Probability, and

Impact models. Outputs are fused into a Risk Index (PoF × Impact) that drives risk-informed maintenance prioritization and supports fleet-scale RCM

The proposed Digital Mirror architecture is distinguished by its hybrid physics-plus-data analytics approach: it blends classical transformer engineering models (thermal aging laws, electrical stress limits, failure rate “bathtub” curves) with data-driven AI/ML algorithms (for anomaly detection, pattern recognition and predictive forecasting). Initial deployment results have demonstrated measurable benefits, including improved transformer availability (projected increase from 99.80% to 99.91%) and significant reductions in maintenance man-hours (25% less outage time spent on interventions). This paper details the Digital Mirror’s design and methodology, the implementation of its key analytical components, and the outcomes observed.