Predicting Bulk Electricity System Reliability Performance Indices Using Sequential Monte Carlo Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
System reliability performance is usually based on average customer interruption information. These average values are valuable information, but provide only a single customer risk dimension without 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 bulk electric systems (BES). 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. Parameter distribution analysis and its potential utilization are relatively new concepts in composite power system reliability analysis and decision making. This paper presents a technique to predict future reliability performance indices of the BES using a sequential simulation approach. The results obtained using the sequential software show that the system performance index probability distributions have unique characteristics that are basically dependent on the system topology and the operating philosophy. Operating policies involving load shedding procedures have a considerable impact on the system performance indices and their associated distributions. Two test systems are used to illustrate the concepts associated with the determination of system performance indices and their associated probability distributions. The results obtained using two different load shedding policies are presented and compared.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it