Sustainable natural ester dielectric liquid for power transformers: Thermo-oxidative performance and kraft paper compatibility
Bibliographic record
Abstract
Natural ester-based insulating liquids have gained significant interest as sustainable alternatives to mineral oils in transformers due to their biodegradability, low toxicity, and favorable dielectric properties. However, their susceptibility to oxidation limits their application, particularly in free-breathing transformers. This study presents the formulation of a novel insulating liquid using canola oil and palm kernel oil methyl ester, enhanced with Tert-butylhydroquinone and 2,6-Di‑tert‑butyl‑4-methyl-phenol antioxidants to improve oxidation stability. The thermal and dielectric performance of the formulated liquid was evaluated through accelerated thermal aging, alongside its compatibility with cellulose insulating paper. Key properties analyzed included density, viscosity, acidity, dielectric dissipation factor, and AC breakdown voltage using Weibull statistical analysis. The synthesized insulating liquid demonstrated superior cooling efficiency, with a viscosity of 12.15 cSt, significantly lower than commercial insulating oil (37.96 cSt). After 40 days of aging, it exhibited a lower density increase (0.22 %) and oxidation rate (66.61 %) compared to commercial oil (0.44 % and 85.72 %, respectively), confirming its improved stability. AC breakdown voltage remained higher than commercial oil, with 54.7 kV after aging. Dielectric spectroscopy of impregnated insulating paper showed no significant variation in dielectric loss and permittivity, confirming the compatibility of the synthesized oil with cellulose insulation. FTIR analysis further validated the structural preservation of kraft paper in the formulated oil. These findings highlight the formulated liquid's enhanced oxidation stability, dielectric performance, and potential as an alternative insulating liquid for industrial 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) | 0.001 | 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".