AI Techniques for Continuous Monitoring of Winding Insulation Health of Transformers

This paper introduces an innovative approach to real-time monitoring of insulation resistance between primary and secondary windings of transformers, which is essential for reliable transformer operation and proactive maintenance. The approach integrates Partial Discharge (PD) Monitoring, Insulation Resistance (IR) Measurement, and Frequency Response Analysis (FRA) with an Artificial Intelligence (AI) model, enabling predictive monitoring of insulation degradation. By continuously analysing operational data, this system identifies potential insulation failures before they occur, supporting safe and cost-effective transformer management. The proposed methodology demonstrates significant improvements over traditional periodic testing approaches, with validation through both simulation and preliminary field testing. This work addresses a critical need in power systems management, offering a practical solution for enhancing transformer reliability through continuous monitoring and predictive analytics.