The Time-Varying Characteristics of Overhead Electric Transmission Lines Considering the Induced-Ice-Shedding Effect
Bibliographic record
Abstract
More ice deposits accreted on conductors or ground wires may be shed off when an overhead electric transmission line is responding to shocks initiated by natural ice shedding. Ice shedding causes the global mass, stiffness, and damping of the tower-line system to vary with time, and the successive shedding effect beyond a trigger event has not been taken into account in previous studies due to the lack of an adequate ice detachment model. In this paper, the ice shedding effect induced by initial shocks was considered in finite element (FE) analysis. An ice detachment criterion, in the way of user-defined element rupture subroutine, was implemented into the main commercial nonlinear FE program ADINA, making it possible to consider the induced-ice-shedding effect numerically. The incremental FE form of the system’s governing equations of motion is presented where the variations in the mass and stiffness matrices of the system are taken into consideration. Taking a transmission line section following natural ice shedding as a case study, the results indicate that neglecting successive ice shedding underestimates the adverse influence of natural ice shedding. The proposed method can help to improve the design and evaluation of transmission lines in cold regions and to ensure their mechanical security.
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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) | 0.001 | 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".