Influence of core window height on thermal characteristics of dry-type transformers
Bibliographic record
Abstract
Elevated temperatures in transformer windings and cores pose a significant risk of damage to power transformers. The objective of this work is to analyze the influence of core window dimensions on the thermal efficiency of power transformers. Analytical approaches are limited in their ability to consider the impact of core window dimensions on the transformer's thermal behavior. Conversely, experimental methods are both expensive and time-consuming. To overcome these constraints, this work assesses and optimizes the temperature distribution in dry-type power transformers using finite element models, specifically examining the impact of the core window. The thermal model treats core and winding losses as sources of heat generation. Four different transformers, with varying heights of the transformer core window, have been modeled to assess the impact of window height on the thermal conditions of the transformers. The simulation findings indicate that variations in core window height have a significant impact on the transformer's thermal properties. By comparing the model's predictions of short-circuit impedance with experimental data, this study demonstrates the model's capability to reliably estimate parameters influenced by core window variations, thereby validating its usefulness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".