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

arXiv: https://arxiv.org/abs/2512.22190 · HTML: https://mlsanity.com/paper/2512.22190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 20 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Uncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studies](https://mlsanity.com/paper/2405.11011/) (2024)
- [Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers](https://mlsanity.com/paper/2503.11398/) (2025)

## Abstract (arXiv)

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.