Improving Oxidation Stability and Insulation Performance of Plant-Based Oils for Sustainable Power Transformers
Bibliographic record
Abstract
In power transformers, insulating liquids are essential for cooling, insulation, and condition monitoring. However, the environmental impact and biodegradability issues of traditional hydrocarbon-based liquids have spurred interest in green alternatives like natural esters. Despite their benefits, natural esters are highly prone to oxidation, limiting their broader use. This study explores a novel blend of two plant-based oils, canola oil and methyl ester derived from palm kernel oil, enhanced with two antioxidants, Tert-butylhydroquinone (TBHQ) and 2,6-Di-tert-butyl-4-methyl-phenol (BHT), to improve oxidation resistance. The performance of this antioxidant-infused oil was evaluated in terms of its interaction with Kraft paper insulation through accelerated thermal aging over periods of 10, 20, 30, and 40 days. Key properties, including the viscosity, breakdown voltage, conductivity, and FTIR spectra of oils, were analyzed before and after aging. Additionally, the degradation of the Kraft paper was investigated using scanning electron microscopy (SEM), optical microscopy, and dielectric strength tests. The results show that the antioxidant-treated oil exhibits significantly enhanced molecular stability, reduced viscosity, lower conductivity, and improved breakdown voltage (53.16 kV after 40 days). Notably, the oil mixture maintained the integrity of the Kraft paper insulation better than traditional natural esters, demonstrating superior dielectric properties and a promising potential for more sustainable and reliable power transformer applications.
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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) | 0.000 | 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".