Improvement of Transmission Line Ampacity Utilization by Weather-Based Dynamic Line Rating
Bibliographic record
Abstract
Most of the existing overhead transmission lines (TLs) are assigned a static rating by considering the conservative environmental conditions (e.g., high ambient temperature and low wind speed). Such a conservative approach often results in underutilization of line ampacity because the worst conditions prevail only for a short period of time during the year. Dynamic line rating (DLR) utilizes local meteorological conditions and grid loadings to adaptively compute additional line ampacity headroom that may be available due to favorable local environmental conditions. This paper details Idaho National Laboratory-developed weather-based DLR, which utilizes a state-of-the-art general line ampacity state solver for real-time computation of thermal ratings of TLs. Performance of the proposed DLR solution is demonstrated in existing TL segments at AltaLink, Canada, and the potential benefits of the proposed DLR for enhanced transmission ampacity utilization are quantified. Moreover, we investigated a hypothetical case for emulating the impact of an additional wind plant near the test grid. The results for the given system and data configurations demonstrated that real-time ratings were above the seasonal static ratings for at least 76.6% of the time, with a mean increase of 22% over the static rating, thereby demonstrating huge potential for improvement on ampacity utilization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".