Application of wellbeing concepts in short term generating unit preventive maintenance scheduling
Bibliographic record
Abstract
Preventive maintenance scheduling of generating units in a deregulated power system is usually conducted on a relatively short-term basis. In these systems, uncoordinated removal of generating equipment for maintenance can result in severe generation shortages. Short term preventive maintenance scheduling is therefore an important requirement in order to avoid excessive price increases and rotating load curtailments. There are a number of different approaches used for preventive maintenance scheduling. The most widely used techniques are deterministically based. Probabilistic approaches, however, have also been used for this purpose, A new methodology has been developed to combine a probabilistic approach and an acceptable deterministic criterion into a single framework. This methodology is designated as the health levelization. technique. The effect on maintenance scheduling of using the tune dependent unit unavailability instead of the forced outage rate (FOR) is illustrated in this paper. The consequences of incorporating load forecast uncertainty (LFU) in the maintenance scheduling process are also examined. The concepts presented are illustrated by application to two test systems: The IEEE Reliability Test System (IEEE-RTS) and the Roy Billinton Test System (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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".