Planification de la remise en charge du réseau d'Hydro-Québec : le système RECRÉ
Bibliographic record
Abstract
Due to the severity of system-wide power outages, though their probability of occurrence is slight, regulatory authorities require that a system restoration plan be drawn up and kept up to date at all times. The power outage that affected northeastern North America in 2003 proved the need for such a requirement. The particular structure of Hydro-Québec's power system requires the use of a highly specific system restoration procedure. The daily preparation of the system restoration plan is based on a strategy whose application requires that a restoration sequence be drawn up that uses available equipment, the electrical behavior of which has been validated using appropriate studies. Over the years, Hydro-Québec has accumulated a sizeable pool of knowledge that brings together all the solutions already studied. System restoration planners use their know-how to find the best solution, determine whether it is appropriate, and proceed to apply it. When required, new solutions must be developed. The new RECRÉ software program (French acronym for "Remise En Charge du Réseau" - System Restoration) has been implemented by Hydro-Québec in 2005. The aim of this knowledge management system is to have system restoration knowledge become a tangible, growth-oriented and enhanceable asset. The system is made up of two modules: the knowledge engineering module, whereby modeling can be done of the system restoration strategy and known solutions in a knowledge base, and the planning module, whereby system restoration plans can be drawn up based on the unavailability of power system equipment. The RECRÉ system can produce a validated and adequate plan in a few seconds along with all the documents required for its dissemination. The RECRÉ system can also analyze in a few minutes all of the situations that apply to scheduled outages for the weeks that follow or the upcoming year. This allows solution gaps to be identified in the knowledge base.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".