Hydro-Québec’s new approach for asset management
Bibliographic record
Abstract
Hydro-Québec TransÉnergie (HQT) has been employing predictive modelling methods to manage its assets for over a decade. To meet new needs and to respond to changes in the energy market, HQT undertakes an important research project in order to improve existing tools for asset management and modelling system. In this paper, we present a quick review of the global asset management model at HQT. Furthermore, we introduce a Contingency Analysis (CA) approach which will be integrated in the reliability simulator module of the HQT global asset management model. To this end, we augment the traditional bus-branch data model to provide a detailed node-breaker representation that contains detailed nodes, breakers and other switching devices. Then, we analyze the impact of the equipment unavailability on the network behaviour using a detailed CA approach. This contingency analysis approach not only informs the asset management engineers in case of violations, but also suggests several remedial actions to eliminate the violation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".