Statistical Analysis of Field Data for Precipitation Icing Accretion on Overhead Power Lines
Bibliographic record
Abstract
This paper addresses the analysis of field data gathered at the Mont Belair icing test site in Quebec. Load cell evaluations of actual icing loads on an existing 315-kV line are correlated to hourly measurements of ambient temperature, wind speed, precipitation rate, and number of signals of the Ice Rate Meter (IRM), in order to establish a numerical model for precipitation icing accretion on overhead line conductors. The correlation analysis is limited to precipitation ice events, or those mixed with relatively short periods of in-cloud icing. Emission of IRM signals is used as a criterion to distinguish the accumulation phase of an ice event, from persistence and shedding, characterized by no emission of IRM signals. The results from the analysis show that the icing rate corresponding to wet growth is much larger than that in dry conditions. What is more surprising, it was also found that the icing rate during periods when winds blow parallel to the line axis is significantly greater than that with perpendicular winds. The linear fit to the set of multivariate data is usually applicable. However, in some relatively rare cases of wet growth in heavy precipitations without IRM signals, the linear model may be inadequate and quadratic polynomials must be used. The results from the application of the numerical model are in excellent agreement with the field observations. This empirical model can be very useful for evaluation of icing loads on energized transmission lines, when there are not available measurements by load cells or other direct methods.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".