A Comprehensive Review on Artificial Intelligence-Based Applications for Transformer Thermal Modeling: Background and Perspectives

• Published in: IEEE access 2024, Vol.12, p.152310-152329
• Main Authors: Pedro da Costa Souza, Joao, Picher, Patrick, Zinflou, Arnaud, Fofana, Issouf, Beheshti Asl, Meysam
• Format: Article
• Language: English
• Subjects: Ambient temperature; Artificial intelligence; Artificial neural networks; Computational modeling; Critical components; Data models; Energy distribution; Fuzzy logic; Fuzzy systems; Load modeling; Loading; Mathematical models; Numerical models; Oil insulation; Power transformers; Predictive maintenance; Support vector machines; Thermal analysis; thermal modeling; Thermal stress; Thermodynamic properties; transformers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3480789

The power transformer is a critical component in any transmission and distribution grid. This vital machine faces new thermal stresses arising from challenges related to energy transition along with the ever-increasing load. Understanding and predicting transformer thermal behavior is fundamental to optimizing operation and maintenance, and consequently ensuring the system's reliability. Transformer thermal modeling (TTM) has garnered significant attention among engineers and researchers. Various approaches to TTM exist, including physical, semi-physical, physical-based numerical, and artificial intelligence (AI)-based models, with the latter being relatively unexplored in the literature. This contribution presents a comprehensive review of AI-based applications for transformer thermal modeling, examining commonly used techniques, inputs, and outputs. Perspectives in the field are discussed, with a focus on gray-box and adaptive models. The impacts of AI-based models in developing digital transformer twins are also explored. Prominent models in TTM include artificial neural networks and fuzzy systems, with support vector regression also featuring among the techniques utilized. Load and ambient temperature are primary inputs in top-oil temperature predictions, while top-oil temperature is crucial for hot-spot temperature predictions. Incorporating historical data is increasingly common in both cases. This review serves as a guide for researchers interested in TTM and highlights perspectives for future developments. AI-based applications offer powerful tools for TTM and, despite present challenges, hold significant potential for transformation in the field.