Distribution systems reliability assessment a new approach for new planning requirements
Bibliographic record
Abstract
This paper presents the new approach developed by HydroQuebec Distribution for the evaluation of the reliability of its distribution MV grid. It tackles the problems and the context which led to this need of renewal, the innovations which were necessary and their application by means of a prototype called FIORD (Fiabilite et Optimisation des Reseaux de Distribution — a French acronym for a global reliability assessment system). Finally it states the advantages of the new approach in the light of the results obtained from its use. The FIORD prototype is inspired by a method of calculation developed by Julien Dallaire (member of Hydro-Quebec Distribution — Vice-presidence Reseau), who played the expert's role for the field of distribution systems planning [1]. Raouf Naggar made the knowledge engineering and Christian Langheit realised the knowledge-based system [2],[3], both are members of Hydro-Quebec — Institut de recherche (IREQ). Note: A French version of this paper may be obtained from the authors / Une copie francaise de cet article peut etre obtenue aupres des auteurs.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".