A comparison between fuzzy and probabilistic estimation of Dynamic Thermal Rating of transmission lines
Bibliographic record
Abstract
Dynamic Thermal Line Rating (DTLR) incorporates weather conditions prevailing on line ampacity to calculate actual current-carrying capacity of transmission lines. These weather variables include ambient temperature, wind speed, wind direction, and solar intensity. In this paper, probability and fuzzy techniques are adopted to model the existing uncertainties in weather variables and their performances are compared. A transmission line in Alberta's electric system is considered as a case study. Weather data are available at nine towers along the transmission line. Line sectional ampacities are estimated using conductor thermal equations. Results indicate that fuzzy model is able to estimate DTLR as efficient as probabilistic model and due to its low computational cost can be used for real-time applications. Modeling uncertainties in DTLR calculation would enable system operators to make decisions on DTLR considering the degree of risk that power utilities are willing to accept.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".