Digital Twin-Based Fault Diagnosis and Resilience Monitoring for Smart Grid Transformer Systems

Abstract

Smart grid transformer systems are critical nodes in national power infrastructure whose failure can cascade into widespread outages, economic losses, and public safety risks. Traditional condition monitoring approaches rely on periodic offline inspections and threshold-based alarms that are ill-suited to the dynamic, real-time nature of modern grid operations. This paper proposes a comprehensive digital twin (DT) framework for fault diagnosis and resilience monitoring of high-voltage power transformers. The architecture integrates physics-based electromagnetic and thermal models, IoT sensor data streams, and machine learning classifiers within a synchronized virtual-physical environment. Five canonical fault modes—partial discharge, winding deformation, insulation degradation, oil contamination, and core saturation—are modeled and validated against historical field data. Experimental results demonstrate an overall fault detection accuracy of 97.3%, a mean time-to-diagnosis of 4.2 seconds, and a false positive rate below 1.8%. The proposed framework reduces unplanned outage risk by an estimated 43% and improves grid resilience indices by 31% compared to conventional SCADA monitoring. The study also discusses integration pathways with CISA cybersecurity mandates and the U.S. Department of Energy's grid modernization initiatives, establishing the framework as a scalable solution for national critical infrastructure protection.