Thermal analysis of power transformers under unbalanced supply voltage
Bibliographic record
Abstract
High temperatures may damage power transformers. These problems leading to the high temperature are more revealed under non‐rating operating conditions such as unbalanced supply voltage. The aim of the present study is thermal analysis of such supply and obtaining its temperature distribution. Existing thermal analysis methods through thermal equivalent circuit (TEC) have some drawbacks; in these models, thermal parameters of different regions of transformer such as core, tank, metallic parts and winding are not defined. On the other hand, those models are not applicable for thermal analysis of transformer with unbalanced supply voltage. Here, a novel TEC model is proposed which is able to define temperatures of different components of oil‐immersed power transformers individually under unbalanced supply voltage. The merit of the introduced model is that losses of different parts of transformer are considered as heat generating sources which are used as the inputs of thermal model. At this end, a three‐dimensional finite‐element method is suggested which is able to estimate the losses of different parts of power transformer. Finally, the results of applying the TEC to the transformer are compared with the temperature distribution of finite‐element modelling and high accuracy of the TEC model in estimation of the temperatures of different regions are emphasised.
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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) | 0.000 | 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 | 0.001 |
| 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".