The Effect of Electronic Scavenger Additives on the AC Dielectric Strength of Transformer Mineral Oil
Bibliographic record
Abstract
This paper is devoted to the influence of two types of electronic scavenger additives/compounds, namely, carbon tetrachloride (CCl4) and methyl iodide, which is also called iodomethane (CH3I), on the dielectric strength of transformer mineral oil. The tests are achieved in a sphere-sphere electrodes arrangement under AC voltage according to the IEC 60156 standard. The investigated additive concentrations range from 0 to 600 ppm. The verification of the conformity of the experimental results with normal and Weibull probabilistic distributions as well as the estimation of the breakdown voltage with risk probabilities of 1%, 10%, and 50% are also performed. It is shown that there is an optimum concentration of each type of electronic scavenger compound at which the dielectric strength of the mineral oil is significantly improved (i.e., it reaches a maximum value). This improvement is of 98% with 500 ppm of CH3I and 93% with 200 ppm of CCl4. It is also shown that the breakdown voltage values of all of the investigated samples with and without additives conform to a Weibull distribution but not to a normal distribution. The obtained results are discussed with regard to the possible mechanisms that may be responsible, particularly the two phases of inception and propagation of the streamers.
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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 | 0.000 | 0.000 |
| 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".