Short-term generating unit maintenance scheduling in a deregulated power system using a probabilistic approach
Bibliographic record
Abstract
Overall evaluation of generating system adequacy appears to be declining in the new utility environment despite the fact that severe power shortages have occurred in jurisdictions such as California and Alberta due to inadequate generating facilities. The installed generating capacity should be capable of meeting the system load in the face of capacity outages and the removal of selected generating units for scheduled maintenance. In a deregulated utility environment, capacity shortages can be created by a lack of coordination in scheduling generating unit maintenance. This can be avoided by having impending maintenance requirements scheduled by the independent system operator. The objective in scheduling preventive maintenance should be to ensure that the resulting risk does not exceed a predetermined acceptable level. In a deterministic approach, the acceptable margin is either, a percentage of the available capacity or load, or a value equal to the largest loaded unit. A methodology for maintenance scheduling is presented that combines a probabilistic approach and an acceptable deterministic criterion into a single framework. This methodology is designated as the health levelisation technique. The effect of conducting preventive maintenance with different load profiles is illustrated. The concepts presented are illustrated by application to the RBTS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".