Transformer Bushing Thermal Model for Calculation of Hot-Spot Temperature Considering Oil Flow Dynamics
Bibliographic record
Abstract
Thermal stress plays a prominent role in the reliability of bushings and contributes to the reliability of power transformers, especially during overload conditions. Thus, exploring the temperature distribution in the bushings is essential. This paper proposes a new thermal model to estimate the hotspot temperature (HST) of oil-impregnated paper (OIP) bushings, based on a modified thermal-electrical analogy model. The proposed model is developed based on the finite element method (FEM) to accurately model all fluid flow and internal convection as well as the thermal conduction mechanism. To this end, convection thermal resistances are defined, and their nonlinear characteristics are calculated for different overloading conditions. The proposed approach is applied on a 245 kV, 800 A OIP bushing to analyze not only the normal loading condition but also short-term overloading beyond the rated current. In addition, the oil flow condition with different load currents and transformer top oil temperatures (TTOT) are investigated. The results show a consistent temperature rise with the typical test conditions with no identified problematic situation. However, during overloading, the temperature rise exceeds beyond the permissible limits recommended by IEEE C57.19. Hence, the overloading of transformers may deteriorate the bushing insulation and reduce its lifetime.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".