Development of a New Bi-Arc Dynamic Numerical Model for Modeling AC Flashover Processes of EHV Ice-Covered Insulators
Bibliographic record
Abstract
This paper presents the development of a new bi-arc dynamic numerical model for predicting AC critical flashover voltage (FOV) of ice-covered extra-high voltage (EHV) insulators. The proposed model is based on a generic calculation algorithm coupled with commercial finite element method software designed to solve the Obenaus/Rizk model. The proposed model allows one to implement the Nottingham and Mayr approaches and compare the results obtained as a function of the arcing distance, the freezing water conductivity, and the initial arc length. The validation of the model demonstrated high accuracy in predicting the FOV of ice-covered post-type insulators and its capability to simulate the interaction of the two partial arcs during the flashover process. In particular, the results showed that the Nottingham approach is sensibly more accurate than the Mayr one, especially in simulating the dynamic behavior of the partial arcs during the flashover process. Based on the encouraging results obtained, a multi-arc calculation algorithm was proposed using the bi-arc dynamic numerical model as a basis. The basic idea, which consists in dividing the multi-arc model in several bi-arc modules, was not implemented and validated but will serve as a promising concept for future work.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".