Identifying critical components for reliability centred maintenance management of deregulated power systems
Bibliographic record
Abstract
Competition in the electric power industry urges utilities to not only reduce the investment costs, but also to reasonably cut the operation and maintenance expenses as much as possible, while keeping both power quality and reliability requirements met. Reliability‐centred maintenance (RCM) has been proven to be in response to this dilemma in power systems and has been yet successfully applied in various engineering contexts. This study introduces a novel approach, as of the first steps of RCM implementation in composite power generation and transmission systems, to identify the critical components for the main sake of a more focused maintenance management. Criticality evaluation is, here, concerned with components outage occurrence possibilities and cost‐based consequences. A realistic market model has been implemented to account for the components outage consequences to the system different participants, that is, generation companies and distribution companies. The presented methodology is able to efficiently recognise the individual importance and contribution of each component in the cases of not only first order but also higher order contingencies. For the sake of demonstration, the proposed approach is applied to the IEEE reliability test system and IEEE 118‐Bus test system, and the obtained results are discussed in detail.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".