Use of artificial intelligence for transformer and reactor health assessment and estimation of ageing

The increasing reliance on power transformers and reactors in transmission and distribution networks necessitates a robust, predictive maintenance strategy to prevent failures, optimize operational efficiency, and extend equipment lifespan. Traditional maintenance approaches, such as time-based preventive maintenance and reactive maintenance, often fail to predict failures accurately, leading to unexpected breakdowns and increased costs. This research proposes an AI-driven transformer and reactor health assessment system that leverages Dissolved Gas Analysis (DGA) data for fault prediction and ageing estimation. The proposed system integrates Machine Learning (ML) and Deep Learning (DL) models to analyze gas concentration trends in transformer oil, which serve as indicators of insulation degradation, overheating, and potential failures. A key innovation of this study is the development of a resampling engine that addresses the challenge of irregular DGA sampling intervals by generating standardized time-series data for forecasting. Various AI models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoregressive Integrated Moving Average (ARIMA), and ensemble learning techniques, are employed to predict transformer health conditions up to 12 weeks in advance. The forecasting pipeline is optimized through cross-validation on rolling time windows, ensuring model adaptability to different transformer ageing patterns. Experimental validation using real-world transformer data demonstrates that the AI-driven system achieves a Mean Absolute Percentage Error (MAPE) below 20%, with an overall forecasting accuracy of 86.78%, surpassing traditional statistical models. The results indicate that AI-powered predictive analytics can effectively detect early signs of insulation breakdown, thermal faults, and electrical discharges, enabling utilities to shift from reactive to proactive maintenance strategies. Furthermore, this research explores the integration of IoT-based real-time monitoring, digital twin technology, and cloud computing to enhance scalability and automation in transformer health assessment. Future advancements may include reinforcement learning models for adaptive maintenance scheduling, blockchain technology for secure data handling, and edge computing for real-time fault detection in decentralized grids. The findings underscore the transformative potential of AI in power infrastructure management, ensuring grid reliability, reducing operational costs, and supporting the transition to a smart, self-healing electrical grid.