Canola oil: A renewable and sustainable green dielectric liquid for transformer insulation
Bibliographic record
Abstract
In the last decades, vegetable-based insulating liquids, derived from plant seeds, have emerged as an environmentally friendly alternative to traditional petroleum-based mineral insulating oils. These vegetable oils exhibit excellent characteristics for high-voltage insulation, including remarkable high-temperature stability, as evident in their flash and fire points. Furthermore, their high water absorption capacity may serve to safeguard the integrity of paper insulation within transformers. However, their practical application is limited to sealed transformers due to their susceptibility to oxidation. Additionally, using these oils in regions with low temperatures presents challenges because of their poor flow properties under cold conditions. Canola oil, derived from canola seeds, offers a balanced set of properties, particularly concerning pour point and oxidation stability, attributable to its unique fatty acid composition. This study reviews deeply into the potential, prospects, and possible enhancements that can be applied to canola oil. Significant tutorial elements as well as some analyses are included. The aim is to reveal the deep attributes of canola oil as a suitable insulating liquid for both free-breathing and hermetically sealed transformers, while also ensuring it serves as an efficient cooling medium for transformers operating in extremely cold environments. Of the many properties examined, this review pays particular attention to oxidation stability and the flow characteristics of the oil.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".