Influence of Fine Metal Particles on Surface Discharge Characteristics of Outdoor Insulators
Bibliographic record
Abstract
Focusing on the influence of fine metal particles on the insulation characteristics of outdoor insulators, spherical micrometer-level iron powders were used to represent fine metal particles of different parameters on a polymer insulator specimen surface. Dynamic movement and lift-off behavior of fine particles, as well as the triggered surface discharges under AC voltage were investigated in a uniform electric field under different experimental conditions. The results reveal that the inception, propagation and intensity of surface discharges are significantly affected by the particle parameters, including particle size, amount and distributing characteristic. Based on the measurement of light emission during the flashover process using a high-speed camera, the process of surface discharge to flashover triggered by the fine metal particles were investigated to obtain a relationship between flashover voltage, discharge light intensity and particle parameters. It is suggested that particle size smaller than 28 µm and particle amount more than 40 mg in contact with the non-uniform distribution can cause a significant distortion and intensification of the electric field resulting in a higher risk of surface discharges leading to flashover. Such investigations can enhance the operating reliability of outdoor insulators subjected to these conditions.
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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 | 0.000 |
| 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".