Bibliographic record
Abstract
Upgrading or constructing new power transmission lines is a very costly and time consuming endeavour. To overcome line capacity problems, some utilities have began to employ an alternative approach of incorporating Dynamic Thermal Circuit Rating (DTCR) technologies into their existing transmission lines to harness underutilized line capacity. However, capacity gains that can be obtained from DTCR technologies may be hampered by certain segments of given transmission line should they have an overall lower ampacity rating compared to the remaining parts of the line. If such bottlenecks exist, the overall ampacity rating of the entire line is decreased. To maximize gains from DTCR technology, this paper presents an intelligent approach of analyzing an existing transmission line and searching out bottlenecks by using high-resolution meteorological data. The system uses an optimization technique to identify segments that will provide the greatest return on investment. Carrying out the suggested upgrades will, in turn, increase the reliability and provide additional transmission capacity gains. Thus, the proposed system will allow utility companies to increase the gains from DTCR technology, while maximizing the return on investment in construction and upgrading projects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".