Spare Assessment of Distribution Power Transformers using Two Markov Models
Bibliographic record
Abstract
In earlier spare assessment studies of distribution power transformers at Hydro One, the issues of the full utilization of mobile unit substations (MUSs) and their reliabilities were not fully addressed and therefore, the results of spare studies may have been underestimated. This paper describes a study that has been performed recently to address the two mentioned issues. The study used a simple and flexible probabilistic approach that shows how the two issues can be properly addressed and helps explain the results of earlier spare studies. The proposed assessment approach uses two Markov models: one representing minor transformer failures and one representing major transformer failures for a group of similar distribution power transformers. The MUS utilization factor introduced in this study is incorporated into each failure model in order to obtain the group availability as a function of the number of spare units. The results of a sample distribution system show that the two issues can have significant impacts on the spare assessment results. The purpose of this paper is to describe the study and its findings and to compare its results with the earlier spare methods of assessment.
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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) | 0.001 | 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 | 0.001 |
| 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".