New experimental study on the DC flashover voltage of polymer insulators: combined effect of surface charges and air humidity
Bibliographic record
Abstract
This research provides conclusions that can partially cover the lack of knowledge related to the effects of ambient atmospheric conditions on DC electric power transportation. Two polymer insulating materials are used for completing this research study. Inside a climate chamber, the relative humidity is controlled and adjusted from 20% to 80% RH. Then, the samples are charged with positive or negative charges by applying to the corona‐generating ring of needles. The surface potential is measured using an electrostatic voltmeter and is converted into surface charge density later by applying the probe response matrix method. The pre‐charged samples are then stressed with high‐voltage negative or positive DC values inside the climate chamber over a range of controlled values of the surrounding air humidity until flashover takes place. The space charges, which can drift in the air gap to reach the solid surface, are highly affected by the level of the relative humidity of the surrounding air. Also, increasing humidity results in a reduction of the DC flashover inception voltage and a shorter time to flashover regardless of the voltage polarity. Moreover, the positive or negative flashover inception voltage of both materials increases with pre‐deposited negative charges while it decreases with pre‐deposited positive charges.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".