Negative DC corona characteristics in SF/sub 6/ under moisture contamination
Bibliographic record
Abstract
This paper reports on the effect of moisture contamination related to negative partial-discharge activity in SF/sub 6/. Moisture contamination is common in gas-insulated systems and is mainly due to 1) temperature-dependent water desorption from inner surfaces and 2) external sources of pollution that infiltrate during installation and operation of the apparatus. However, few studies have been published on the subject and even they have focused on positive or AC voltages. Little is known about the influence of water vapor on negative partial-discharge phenomena, or its physical reality. The aim of this work, therefore, is to identify the influence of water contamination on a negative partial-discharge regime from electrical measurements and to corroborate our observations with known data. Any observations of negative effects from water contamination and/or methods of detecting water contamination by means of electrical measurements on gas-insulated systems could be of great help to engineers. The experiment was conducted under true-corona conditions; for that particular arrangement, the breakdown occurred in the range of 25 kV for atmospheric SF/sub 6/. The characterization study focused mainly on the corona current and the distribution of recorded pulse amplitudes and frequencies. The breakdown and inception voltages were also measured. The results of all these measurements, taken separately in pure and contaminated SF/sub 6/ are compared.
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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) | 0.002 | 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".