Écoulement de puissance optimal avec contraintes de stabilité transitoire pour réseaux hydrothermiques
Bibliographic record
Abstract
Depuis plusieurs années, il est devenu difficile de justifier la construction de nouvelles infrastructures de transport d’électricité. Il est donc primordial d’utiliser ces installations au maximum de leur capacité. Afin d’y arriver, ce mémoire propose un algorithme permettant d’obtenir un écoulement de puissance optimisé avec contraintes de stabilités transitoires. Cet algorithme utilise le critère traditionnel de la stabilité angulaire pour établir si le réseau reste stable durant la période transitoire. Toutefois, ce critère n’est pas le seul pris en compte. Un critère additionnel de tension minimale est considéré afin d’obtenir un réseau électrique respectant des critères plus près de la réalité des exploitants de réseaux électriques comme Hydro-Québec. L’algorithme propose une approche itérative qui alterne entre calcul d’écoulement de puissance optimisé et simulation dynamique afin de converger vers un écoulement optimal sécuritaire.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".