Use of Markov Models in Assessing Spare Transformer Requirements for Distribution Stations
Bibliographic record
Abstract
This paper describes a probabilistic method based on Markov models for assessing the number of spare transformers, regular units and mobile units, required for a group of distribution transformers. The proposed method uses two criteria in determining the required number of spare transformers. The first criterion assumes that a pre-determined level of the group availability is given and the number of spare units is determined when the calculated group availability exceeds the pre-determined level of the group availability. The second criterion uses a cost/benefit analysis method in calculating the number of the spare units. In the second criterion, the number of spare units (optimal number) is determined when the total cost (spare unit capital costs and unit outage costs) is minimum. The proposed method is also used to evaluate the impacts of multi-transformer stations (stations with transformer redundancy) and station capabilities for use of mobile unit transformers on the number of spare units. Two distribution transformer groups of the Hydro One's distribution system are used to illustrate the proposed method of assessment and to compare the results obtained using the two criteria.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".