Power transformer health index and life span assessment: A comprehensive review of conventional and machine learning based approaches

2025-01-01

Engineering applications of artificial intelligence

AI Summary

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.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Problem

• Assessing power transformer health and lifespan using conventional and machine learning methods
• Evaluating merits and drawbacks of AI techniques for transformer fault diagnosis
• Improving diagnostic accuracy and early fault detection via AI and time-series analysis

Innovation

• Uses AI like ANN and CNN for fault diagnosis
• Combines multiple AI methods for better accuracy
• Applies time-series analysis for early fault detection