Bibliographic record
Abstract
This paper presents a reliability based method for spare planning of power system equipment.It models both repairable and aging failure modes of equipment.Two spare analysis methods have been discussed.One is based on the equipment group reliability criterion and another one on the probabilistic cost model.The two methods are incorporated into a uniform procedure and coordinated each other.A spare plan obtained using the presented method includes the numbers of spares and timing of each spare in a long term planning period.It also provides a spare plan for a short term.Another feature of the method is its capacity to perform a probabilistic benefit/cost analysis for spare plans,which provides a quantified financial justification in decision-making.An example of 16 transformers as an equipment group has been used to demonstrate details of the method.This is an actual application in BCTC of Canada.In this example,two long-term and two short-term spare plans are obtained.The group reliability levels and benefit/cost ratios of the spare plans have been compared.The presented method and the calculation procedure are general and can be applied to spare planning of any power system equipment.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".