Reliability-performance-index probability distribution analysis of bulk electricity systems
Bibliographic record
Abstract
System reliability performance is usually based on average customer-interruption indices. The average values are valuable information, but provide only a single customer risk dimension without the underlying probability distributions. The average annual indices give no insight as to how reliability may vary from year to year as a result of the random behaviour of a bulk electric system. Reliability-index probability distributions, therefore, provide additional valuable information and a more complete understanding of composite power system behaviour. A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability-index probability distributions in addition to the expected values of their indices. This paper illustrates the development of probability distributions for bulk electric system reliability performance indices using sequential simulation. The results obtained using the developed software show that the system performance-index probability distributions have unique characteristics that are basically dependent on the system topology, operating philosophy and conditions. System conditions such as the peak load level and system reinforcement options have significant impacts on the performance-index probability distribution characteristics. The basic concepts and their application in composite power system reliability evaluation are illustrated by application to a small practical test 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".