Challenge of mixed insulating liquids for use in high-voltage transformers.1. Investigation of mixed liquids
Bibliographic record
Abstract
The aim of this work is to present results of investigations into mixtures of two insulating liquids, recently proposed as alternatives to mineral oil. The mixtures are a combination of the widely available mineral oil and a specific amount of ester liquid, which has similar electrical properties combined with fewer environmental risks but high hygroscopicity. The water saturation limit of esters is more than 40 times larger than that of mineral oils. Esters absorb water vapor from the air in larger quantities than mineral oil, and this hygroscopicity reduces the moisture content in solid insulation due to diffusion from the solid into the liquid, while the dielectric properties of ester liquids are only slightly changed . Although the life of an oil in service depends primarily on its initial quality, service conditions need to be considered also. The investigations have therefore been carried out on unaged mixed liquids as well as on specimens under severe ageing conditions. Pure liquids have also been investigated to provide baseline data for comparison purposes. The first part of the investigation compares the properties of the mixed liquids with those of pure liquids. The second part of the investigation, will evaluate the compatibility of the mixed liquids with insulating papers used in high-voltage transformers.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".