用神经网络提升变压器健康监测精度，融合物理规律与数据驱动
Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part I: Basic Concepts, Neural Networks, and Variants
- 用卷积神经网络处理多源传感器数据，捕捉故障特征
- 将物理知识嵌入模型，增强对复杂工况的适应能力
- 适合电力系统运维和智能诊断研究者参考
电力变压器是电网中的关键资产，其可靠性直接影响电网韧性与稳定。传统监测方法多基于规则或纯物理模型，面临不确定性高、数据少、现代运行条件复杂等挑战。机器学习技术为弥补和扩展这些方法提供了强大工具，可实现更精准的故障诊断、寿命预测与控制。本系列论文分两部分，探讨神经网络及其变体在变压器状态监测与健康管理中的应用。本文介绍神经网络基础概念，研究卷积神经网络（CNN）在多模态数据下的状态监测性能，并讨论将神经网络思想融入强化学习框架以支持决策与控制。最后还展望了新兴研究方向。
原文摘要 · Abstract (English)
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.
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