Natural Esters for Green Transformers: Challenges and Keys for Improved Serviceability
Bibliographic record
Abstract
The service of mineral insulating oils for power transformer insulation and cooling aspects cannot be disavowed. However, the continued use of mineral oils is questionable due to environmental unfriendliness and the divestment from fossil fuels. This has provoked the quest for green alternative insulating liquids for high-voltage insulation. Natural esters are among the remaining alternatives that are renewable and environmentally friendly. Regardless of their environmental and technical merits, natural esters have some limitations that are slowing down their total acceptance by transformer owners and utilities. Critical limitations and concerns include esters’ pour point, viscosity, oxidative stability, and ionization resistance. In this work, the state of the art of “natural esters for transformers” is explored with the aim of potential improvements. The sections of the article are geared towards technical viewpoints on improving the overall workability and serviceability of natural esters in high-voltage applications. A comprehensive review of the existing literature is achieved, based on performance improvements of the natural ester using “additives” and “chemical modification”. The authors hope that this report may be helpful to transformer owners as well as influence the progression of natural esters for power transformer applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".