Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part I: Basic Concepts, Neural Networks, and Variants

TL;DR

This paper explores how neural networks, including CNNs and reinforcement learning, can enhance transformer condition monitoring by addressing uncertainties and data limitations in modern power systems.

Contribution

It introduces neural network concepts and their applications in transformer monitoring, highlighting new integration approaches with reinforcement learning for improved decision-making.

Findings

Neural networks improve diagnostic accuracy in transformer monitoring.

CNNs effectively process diverse data modalities.

Reinforcement learning offers promising control strategies.

Abstract

Power transformers are critical assets in power networks, whose reliability directly impacts grid resilience and stability. Traditional condition monitoring approaches, often rule-based or purely physics-based, struggle with uncertainty, limited data availability, and the complexity of modern operating conditions. Recent advances in machine learning (ML) provide powerful tools to complement and extend these methods, enabling more accurate diagnostics, prognostics, and control. In this two-part series, we examine the role of Neural Networks (NNs) and their extensions in transformer condition monitoring and health management tasks. This first paper introduces the basic concepts of NNs, explores Convolutional Neural Networks (CNNs) for condition monitoring using diverse data modalities, and discusses the integration of NN concepts within the Reinforcement Learning (RL) paradigm for decision-making and control. Finally, perspectives on emerging research directions are also provided.