A Novel Methodology to Incorporate Circuit Breaker Active Failure in Reliability Evaluation of Electrical Distribution Networks
Bibliographic record
Abstract
The random failure of components in a distribution network leads to power supply interruptions to electricity customers. Among different failure modes in the distribution network components, active failure is more frequent and requires the circuit breaker operations to isolate faulty segments. Active failure of a breaker causes the operation of a backup breaker, thus, exposing the larger segment of the network to outages. This paper proposes a new analytical methodology to identify breaker active failure events involving different order of contingencies. This methodology introduces an active breaker incidence (ABI) matrix to capture the active failure of breakers leading to load point failures. The ABI matrix is concatenated to the incidence matrix of the minimal path to form a new incidence matrix which reflects the information of all failure events including active failure of circuit breakers. These failure events are then utilized to evaluate the reliability indices. The proposed methodology is illustrated in a test distribution network. A study conducted on the IEEE Gold Book Standard Network shows that that the methodology effectively identifies and includes breaker failure events to evaluate the reliability performance, and that the proposed methodology can be utilized to make investment decisions in modern industrial distribution systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".