A Simple Thermoelectrical Surface Approach for Numerically Studying Dry Band Formation on Polluted Insulators
Bibliographic record
Abstract
This paper presents a simple thermoelectrical temporal surface method for numerically studying the appearance of a dry band on a polluted insulator. The proposed method combines an empirical expression of the pollution layer surface conductivity, expressed as a function of the temperature and equivalent salt deposit density (ESDD), and a surface approach for modeling the pollution layer, using thermoelectrical temporal simulations based on the finite element method (FEM). Using different material substrates, pollution layer thicknesses, and ESDD levels, the reliability and limitations of the simple thermoelectrical numerical model have been studied. The numerical results obtained demonstrated that the proposed thermoelectrical model can dynamically simulate the dry band appearance in accordance with the experimental results in terms of the temporal evolution of the temperature and the pollution layer resistance, as well as the evolution of the voltage drop and E-field along the dry band formation zone. The results also demonstrate the influence of the material substrate and the pollution layer thickness, which directly influence the thermal aspect of the dry band formation. The simple thermoelectrical numerical surface model was used to study the dry band appearance on a uniformly polluted 69 kV insulator. The results obtained enabled a dynamic simulation of the appearance of the first dry band, which appeared in the middle of the insulator, and to deeply investigate the evolution of the surface temperature, electric potential, and E-field distributions along the insulator. The proposed simple thermoelectrical model combined with the empirical model is able to simulate the influence of a non-uniform pollution layer. Hence, the proposed model provides a simple numerical tool for studying the evolution of the potential and E-field distributions along uniformly and non-uniformly polluted insulation equipment to identify the probability of a region of high dry band appearance relative to the insulator material and geometry. This can aid in the development of new types of mitigation methods to improve the performance of all types of insulators under polluted 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".