Transmission line ampacity improvements of altalink wind plant overhead tie-lines using weather-based dynamic line rating
Bibliographic record
Abstract
Overhead transmission lines (TLs) are conventionally given seasonal ratings based on conservative environmental assumptions. Such an approach often results in the underutilization of the overhead TL capacity as the most conservative environmental conditions occur only for a short period over an year/season. We present computational fluid dynamics (CFD) enhanced weather-based dynamic line rating (DLR) as an enabling smart grid technology that adaptively computes ratings of TLs based on local weather conditions to utilize the additional headroom of line ampacity due to concurrent cooling of existing lines. In particular, a general line ampacity state solver is proposed to utilize measured weather data for computing the real-time thermal rating of the TLs. The performance of the proposed CFD enhanced weather-based DLR is demonstrated from a field study of DLR technology implementation on four TL segments at AltaLink, Canada. The performance is evaluated by comparing the existing static and the proposed dynamic line ratings, and the potential benefits of DLR for enhanced transmission assets utilization are quantified. For the given line segments, the proposed DLR results in real-time ratings above the seasonal static ratings for most of the time (up to 95.1%) with a mean increase of 72% over static rating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".