Study Explores Physics-Informed Machine Learning for Power Transformer Monitoring

Global: Study Explores Physics-Informed Machine Learning for Power Transformer Monitoring

Researchers led by Jose I. Aizpurua have released a new study that examines how physics-informed machine learning can enhance condition monitoring of power transformers. The paper was submitted to arXiv on 20 Dec 2025 and later presented at the 8th International Advanced Research Workshop on Transformers (ARWtr) in Baiona, Spain, 2025. Its goal is to improve diagnostic and prognostic capabilities for these critical grid assets.

Background on Transformer Monitoring

Power transformers serve as pivotal components in electrical transmission networks, directly influencing grid resilience and stability. Maintaining their health is essential to prevent outages and costly repairs.

Limitations of Conventional Methods

Traditional monitoring approaches rely on rule‑based or purely physics‑based models, which often struggle with uncertainty, sparse data, and the complexity of modern operating conditions. These constraints motivate the exploration of data‑driven techniques.

Machine Learning Techniques Presented

The authors introduce fundamental concepts of neural networks (NNs) and evaluate convolutional neural networks (CNNs) for processing diverse sensor data. They also discuss how NN principles can be embedded within reinforcement learning (RL) frameworks to support decision‑making and control strategies in transformer health management.

Key Findings and Future Directions

According to the abstract, the study outlines basic NN architectures, demonstrates CNN applicability to multimodal monitoring data, and highlights the potential of RL for adaptive control. The authors conclude with perspectives on emerging research avenues, such as hybrid physics‑ML models and real‑time deployment.

Publication and Peer Context

The work appears in the conference proceedings of ARWtr, spanning pages 87‑94, and is indexed under the Machine Learning (cs.LG) subject area on arXiv. A DOI (10.48550/arXiv.2512.22190) provides a permanent link to the preprint.

Implications for Power Grid Reliability

If validated in operational settings, the proposed methods could enable more accurate fault detection, extend equipment lifespans, and support proactive maintenance, thereby strengthening overall grid reliability.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.