A Review of Physics-Informed and Data-Driven Surrogate Models for Power Transformer Fault Diagnosis

Power transformers are critical components of modern power grids, and their operational reliability directly affects power system security, stability, and continuity. With the increasing intelligence and complexity of power systems, condition monitoring and fault diagnosis of transformers have received growing attention. However, conventional diagnostic methods often face limitations such as complex modeling procedures, high computational costs, weak adaptability, and insufficient generalization under nonlinear and coupled operating conditions. In recent years, surrogate models have emerged as effective tools for transformer fault diagnosis because of their advantages in high-dimensional nonlinear mapping, rapid prediction, and data-driven approximation. This paper systematically reviews the research progress of surrogate models in transformer fault diagnosis and establishes a classification framework from the perspectives of model types, modeling strategies, data sources, and application scenarios. The principles, applicable conditions, and performance characteristics of representative surrogate models are comparatively analyzed. Furthermore, the advantages and limitations of different surrogate modeling approaches are discussed in terms of diagnostic accuracy, stability, generalization ability, interpretability, and computational efficiency. Although surrogate models show strong potential for intelligent transformer fault diagnosis, challenges remain in small-sample learning, data quality dependence, model interpretability, and cross-condition generalization. Future research should focus on multi-source information fusion, integration of physical mechanisms with data-driven learning, lightweight intelligent modeling, and standardized evaluation systems. This review aims to provide methodological guidance and technical references for the development of reliable, efficient, and interpretable transformer fault diagnosis methods.