Comparative Study of Breakdown Voltage of Mineral, Synthetic and Natural Oils and Based Mineral Oil Mixtures under AC and DC Voltages
Bibliographic record
Abstract
This paper deals with a comparative study of AC and DC breakdown voltages of based mineral oil mixtures with natural and synthetic esters mainly used in high voltage power transformers. The goal was to analyze the performances of oil mixtures from the dielectric withstand point of view and to predict the behavior of transformers originally filled with mineral oil and re-filled with synthetic or natural ester oils when emptied for maintenance. The study concerns mixtures based on 20%, 50%, and 80% of natural and synthetic ester oils. AC breakdown voltages were measured using a sphere-sphere electrode system according to IEC 60156 specifications; the same specification was adopted for DC measurements since there is no standard specifications for this voltage waveform. A statistical analysis of the mean values, standard deviations, and histograms of breakdown voltage data was carried out. The Normal and Weibull distribution functions were used to analyze the experimental data and the best function that the data followed was used to estimate the breakdown voltage with risk of 1%, 10%, and 50% probability. It was shown that whatever the applied voltage waveforms, ester oils always have a significantly higher breakdown voltage than mineral oil. The addition of only 20% of natural or synthetic ester oil was sufficient to considerably increase the breakdown voltage of mineral oil. The dielectric strength of such a mixture is much higher than that of mineral oil alone and can reach that of ester oils. From the point of view of dielectric strength, the mixtures constitute an option for improving the performance of mineral oil. Thus, re-filling of transformers containing up to 20% mineral oil residues with ester oils, does not present any problem; it is even advantageous when considering only the breakdown voltage. Under AC, the mixtures with natural ester always follow the behavior of vegetable oil alone. With the exception of the 20% mixture of natural ester in DC, the breakdown voltage values of all the tested mixtures were in accordance with the normal distribution, which made it possible to define the breakdown voltages for the risk levels of 1%, 10%, and 50% of probability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".