Evaluating thermal aging characteristics of electric power transmission lines
Bibliographic record
Abstract
Assessment of aging characteristics of conductors and other components of power transmission networks plays an important role in asset management systems. Due to adverse effects of conductor aging caused by annealing, the conductors lose their tensile strength. Although the loss of strength is gradual, it accumulates over time and increases the probability of outages and blackouts. Therefore, the most important factor affecting the strength of transmission conductors is the operating temperature of the line. For this reason, it is important to keep track of conductor temperatures over time, in order to identify segments of power transmission network that may require more close attention, and possibly repairs. This paper describes and illustrates a new methodology for estimating conductor thermal aging using load information and weather conditions derived from historical weather reanalysis, and interpolated to locations of power transmission lines. Conductor temperature is first determined using IEEE 738 standard, and then used to estimate loss of tensile strength in a conductor. The process is illustrated for a single location of a sample transmission line, using assumed load current and historical weather information spanning a period of five years. The simulation results show that the proposed approach provides information vital for transmission asset management and transmission network operating procedures.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".