Insulator Icing Test Methods, Selection Criteria and Mitigation Alternatives
Bibliographic record
Abstract
IEEE-PES Task Forces on insulator icing have been active since 1999 in developing icing test methods and recommending insulator selection criteria. Progress towards this goal included methods for obtaining reproducible results and also modeling of important factors. This paper reports on the progress and work accomplished by the PES Task Forces, serving as input data to the joint DEIS/PES task force work initiated recently to expand the technical basis of the existing knowledge. In selecting adequate insulators for substations and lines at distribution and transmission voltage levels, special measures may be needed in locations exposed to freezing conditions. The environmental and insulator parameters that influence the risk of flashover are noted. The selection process and mitigation options, based on these environmental parameters, are then described. The selection criteria include insulator size, shape, surface material, surface quality, electric field improvement, and orientation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".