A Comprehensive Review on Artificial Intelligence-Based Applications for Transformer Thermal Modeling: Background and Perspectives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".