Methodology to incorporate stuck condition of the circuit breaker in reliability evaluation of electrical distribution networks
Bibliographic record
Abstract
The random failure of a circuit breaker (CB) in a power distribution network adversely impacts its ability to maintain supply continuity to its electricity customers. Among different CB failure modes, the failure to open, or the stuck condition, of a normally closed CB causes the operation of its backup breaker(s) leading to outages of multiple healthy feeders. Stuck condition is a low‐probability failure event, but has wide‐spread consequences when it occurs. This study proposes a generalised analytical methodology to identify the failure events due to the stuck breaker condition. The complexity of the reliability evaluation of a modern distribution system involving multiple switching arrangements is achieved through a search algorithm and repeated matrix operations that can readily be implemented in a computer program. The methodology is illustrated using a test distribution network. The usefulness of the methodology is demonstrated by quantifying the impact of the stuck breaker condition on load point and system reliability indices. The developed methodology can be a useful tool for industrial/commercial customers and utilities to incorporate the impact of breaker failures, including stuck breaker conditions, and make wise investment decisions in the upgrade and maintenance of the distribution network components and the protection system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".