Application of wellbeing concepts in short term generating unit preventive maintenance scheduling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it