A two-stage framework for power transformer asset maintenance management—Part II: Validation results
Bibliographic record
Abstract
A two-stage framework for transformer maintenance management is introduced and formulated in Part I of this two-part paper in the context of transmission asset management strategies (TAMS). The proposed model optimizes maintenance outage schedule over a predefined period of time by taking into account the actual and expected transformer assets' condition dynamics in terms of failure rate and resource limitations in midterm horizons, as well as operating constraints, economic considerations and N-1 reliability in the shorter term. In Part II, a small six-bus system is first used to demonstrate how the two-stage maintenance framework works using a step-by-step procedure. Then, IEEE-RTS is used to investigate the performance of the proposed model in more detail. In addition, the impacts of varying the characteristics of the proposed midterm and short-term maintenance schedulers, such as flexibility in time horizon selection, on maintenance scheduling results and computational efficiency are investigated on IEEE-RTS. The numerical studies indicate that the proposed framework gives appropriate results in terms of economics and technical constraints at a reasonable computational cost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".