Effect of cold fog on leakage current characteristics of polluted insulators
Bibliographic record
Abstract
Due to the wide range applications of high voltage insulators in power network systems, the reliable performance of insulators is challenged by different climate conditions including soluble and non-soluble contaminations. High voltage insulators installed near coastal areas are heavily contaminated with sea salt. In winter, due to the combined effect of sea salt and cold fog deposition, leakage current (LC) may flow on the insulators and surface degradation may take place, which may lead to surface flashover and consequently, to premature failure of power network systems. The leakage current waveform has a strong relationship with the contamination level, fog conditions and applied voltage. The investigation of LC before the flashover is essential to the reliability and secure operation of outdoor insulators. To determine the leakage current (LC) performance of high voltage silicone rubber insulators, laboratory investigation was carried out in an environmental chamber to understand this phenomenon and to simulate different behavior on the insulator surface. The performance of LC was investigated in laboratory during three different periods i.e. wetting period, drying period and dry band arcing period. Also for better understanding of insulator leakage current response an electrical model was used for computer based simulation using EMTP software. The relationship was analyzed on experimental and simulation work on the bases of magnitude and total harmonic distortion (THD).
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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".