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 on how reliability may vary from year to year as a result of the random behavior of a bulk electric system. Reliability index probability distributions, therefore, provide additional valuable information and a more complete understanding of composite power system behavior. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".