Spare Assessment of Distribution Power Transformers Considering the Issues of Redundancy and MUS Capability
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
Probabilistic methods based on Markov models were developed in the last few years to determine the optimal number of spare transformers (regular spare units and mobile substation units) for a group of similar distribution power transformers. These methods accounted for a number of factors that affected the number of spare transformers. The two issues that would have an impact on the number of spare units are transformer redundancy and whether or not the station has a mobile unit substation capability. These two issues were not fully investigated in the earlier spare assessment work and need to be studied in more detail. This paper describes the recent reliability study performed at Hydro One to address the two issues. The study includes a review of three spare assessment methods, two existing and one new, and a detailed analysis of how each method handles the two issues. Examples are provided to illustrate and compare the various assessment methods and to draw some recommendations with regard to the use of these methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it