Thermal Behavior of Power Transformers Filled With Waste Vegetable Oil-Based Biodiesel Under Dynamic Load
Bibliographic record
Abstract
Abstract Petroleum-based oils are widely used as electrically insulating materials in high voltage power transformers for dissipating high generated heat flux and maintaining the temperature below critical values. The operating temperature of a transformer dominantly governs its aging rate. In the present research, a renewable coolant as a versatile substitution for the petroleum-based oils was investigated to be employed in the cooling of transformers. The studied coolant is a vegetable-based oil extracted from the waste cooking oils. A numerical model was developed to follow the instantaneous changes in the load profile and ambient temperature and predict the instantaneous hotspot temperature values in the transformer under dynamic load. Then, this thermal model was used to explore the capability of the studied vegetable oil in the cooling of transformers compared with conventional transformer oil. The realistic ambient temperature and loading profile, as well as thermal properties of oils and characteristics of a transformer, were applied as the model’s inputs. The aging rate of the transformer in the presence of vegetable oil was also compared with the conventional transformer oil. The results indicate a better cooling performance for the vegetable-based oil, where a hotspot temperature reduction of 3 °C was observed in comparison to the petroleum-based oil. Also, the model predicts a significantly longer life for the insulating system of the transformer when the proposed vegetable-based oil is employed. The results of this research suggest a sustainable way of reusing the waste of a renewable resource as an alternative insulating liquid for the cooling of high heat flux electric/electronic devices.
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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) | 0.001 | 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) | 0.002 | 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".