Power transformer health index and life span assessment: A comprehensive review of conventional and machine learning based approaches
Accurate health assessment and remaining useful life (RUL) prediction of power transformers remain challenging due to heterogeneous diagnostic data and model interpretability limitations. Method: This paper systematically reviews and unifies health index construction principles and RUL prediction paradigms, integrating traditional diagnostic data—including dissolved gas analysis (DGA), frequency response analysis (FRA), and dielectric loss—and machine learning models such as SVM, random forests, LSTM, and graph neural networks into a multi-source information fusion classification framework. Contribution/Results: It proposes novel applicability criteria distinguishing physics-based and data-driven methods, revealing synergistic modeling opportunities. Emphasizing interpretable modeling as critical for assessment robustness, the study identifies hybrid modeling—combining physical constraints with deep representation learning—as the key pathway toward high-accuracy, high-fidelity transformer health assessment.