Distribution System Reliability Assessment Incorporating Weather Effects
Bibliographic record
Abstract
Electrical distribution systems usually exist in outdoor environments. The weather creates varying degrees of physical stress on system components exposed to fluctuating weather conditions. The failure rate of an element is greatly enhanced in bad weather situations and the likelihood of multiple line failures is much higher in bad weather than in normal weather. The phenomenon of coincident failures of two or more circuits as a result of excessive stresses imposed by weather conditions is designated as failure bunching. Power supply reliability is normally improved by system redundancy and multiple circuit failures severely impact the system reliability. Most customer interruptions are due to problems that arise in distribution systems and a large number of supply outages occur during unfavourable weather situations. Reliability evaluation disregarding weather effects can result in highly inaccurate appraisals. This paper presents an assessment associated with a simple distribution network comprised of two and three line parallel redundant supplies. A series of reliability indices obtained using single state, two state and three state weather models are presented. The results shown illustrate that stress related line failures due to bad weather should be incorporated in practical assessments and that bad weather situations should be divided into at least two categories
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".