Mechanism of saline deposition and surface flashover on outdoor insulators near coastal areas part II: Impact of various environment stresses
Bibliographic record
Abstract
This is the second in a two-part paper series dealing with sea salt transportation and deposition mechanisms, and discussing the serious issue of degradation of outdoor insulators resulting from various environmental stresses and severe saline contaminant accumulation near the shoreline. The deterioration rate of outdoor insulators near the shoreline depends on the concentration of saline in the atmosphere, influence of wind speed on the production of saline water droplets, moisture diffusion and saline penetration on the insulator surface. This paper comprises two parts. The first part, deals with the impact of different environmental stresses on insulator surface degradation, including wind speed and direction, cold fog and rainfall. The second part concerns the flashover process related to saline contamination of the surface under constant and variable cold fog wetting rates and equivalent salt deposit density (ESDD). The experiments were performed on high voltage insulators based on the model presented in Part-I. Based on the proposed model, the influence of wind speed and direction on the pollution accumulation rate and impact of wetting rate on discharge current and surface flashover process were investigated. The equations S=S0e(Vdep0/αh)[e(-αx/v)-1] and D=D0e(Vdep0/αh)[e(-αx/v)-1] are derived from the model for saline concentration and deposition show good reliability and well represent the results obtained. Test results also show that due to the different wetting and contamination deposition rate, surface discharge current characteristics of tested insulator in rain are different with that in cold fog, which lead to different surface flashover voltages. An experimental setup was mounted for artificial saline contamination deposition. The proposed model can be therefore used to investigate insulator flashover near coastal areas and for mitigating saline flashover incidents.
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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) | 0.002 | 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".