Enhancing the Performance of Natural Ester Insulating Liquids in Power Transformers: A Comprehensive Review on Antioxidant Additives for Improved Oxidation Stability
Bibliographic record
Abstract
The reliability of the electrical grid is vital to economic prosperity and quality of life. Power transformers, key components of transmission and distribution systems, represent major capital investments. Traditionally, these machines have relied on petroleum-based mineral oil as an insulating liquid. However, with a global shift toward sustainability, renewable insulating materials like natural esters are gaining attention due to their environmental and fire safety benefits. These biodegradable liquids are poised to replace hydrocarbon-based oils in transformers, aligning with Sustainable Development Goals 7 and 13 by promoting clean energy and climate action. Despite their advantages, natural esters face challenges in high-voltage applications, particularly due to oxidation stability issues linked to their fatty acid composition. Various antioxidants have been explored to address this, with synthetic antioxidants proving more effective than natural ones, especially under high-temperature conditions. Their superior thermal stability ensures that natural esters retain their cooling and dielectric properties, essential for transformer performance. Furthermore, integrating machine learning and artificial intelligence in antioxidant development and monitoring presents a transformative opportunity. This review provides insights into the role of antioxidants in natural ester-filled power equipment, supporting their broader adoption and contributing to a more sustainable energy future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".