On the Performance of Particle Contaminated GIS with Coated Electrodes
Bibliographic record
Abstract
Electrical insulation performance of compressed gas insulated switchgear (GIS) and gas insulated transmission line (GITL) system is adversely affected by metallic particle contaminants. Dielectric coatings help to improve the insulation performance in several ways. For example, it is known that dielectric coated electrodes in compressed gas give a somewhat higher breakdown voltage. Such coatings have the effect of “smoothing” the surface and reducing the pre-breakdown current in the gas gap. Also, in the presence of metallic particle contamination, the electrostatic particle charging is impeded; hence, the maximum particle excursion in a coaxial GIS/GITL is significantly reduced for a given applied AC voltage. A simple particle charging model, however, has some significant shortcomings. For example, the charge exchange mechanism between the particle and the electrodes is poorly understood and very complex to model. In this article, the dynamics of a wire particle in a coaxial GITL system with coated electrodes under AC voltage is studied using a computational algorithm. The possibility of SF 6 gas insulation breakdown due to the presence of metallic contaminants was computed at different applied voltages and gas pressures. Results of laboratory experiments conducted at Chalmers University to verify the computational outcome are presented.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".