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

2025-12-20

AI Summary

To address the poor interpretability and robustness of conventional power transformer condition monitoring methods under uncertainty, limited data, and complex operational conditions, this paper proposes a physics-informed hierarchical intelligent diagnostic framework. The framework uniquely integrates physical constraints into neural networks, combining CNN-based multi-source feature extraction (dissolved gas analysis, acoustic emission, infrared thermography), multimodal data fusion, and a reinforcement learning–driven decision mechanism to enable closed-loop modeling from perception to control. Compared with purely data-driven or purely physics-based approaches, the proposed method significantly enhances generalization capability under small-sample and noisy conditions. It achieves over 92% fault identification accuracy while ensuring high precision, strong interpretability, and engineering deployability. This work establishes a novel paradigm for transformer health management across its entire lifecycle.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Feature Construction/Reformulation

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Developing physics-informed machine learning for transformer condition monitoring
• Integrating neural networks with reinforcement learning for decision-making
• Enhancing diagnostics and prognostics using diverse data modalities

Innovation

• Neural Networks for transformer condition monitoring
• Convolutional Neural Networks using diverse data modalities
• Integration of Neural Networks with Reinforcement Learning