Short-Term Reliability Evaluation of Generating Systems Using Fixed-Effort Generalized Splitting
Bibliographic record
Abstract
The short-term reliability evaluation techniques provide a rational approach for risk-informed decision making during power system operation. The existing reliability assessment techniques involve large computational burden and therefore are not directly applicable for short-term reliability evaluation during system operation. To this end, this paper presents a computationally-efficient approach for short-term reliability evaluation of wind-integrated generating systems. The proposed approach makes use of the fixed-effort generalized splitting (FEGS) technique, which is a variant of importance splitting. To realize the implementation of FEGS, a discrete version of component-wise Metropolis-Hastings (MH) algorithm for Markov Chain Monte-Carlo (MCMC) is also presented. Besides, the proposed FEGS approach is extended to take the uncertainties of wind generation and load demand into account. The simulation results indicate that, in comparison to crude Monte-Carlo simulation (CMCS), the proposed approach is able to evaluate short-term reliability indices with a low computational burden. Moreover, further simulation results indicate the impacts of uncertainties of wind generation and load demand on short-term reliability indices.
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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 | 0.002 | 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 | 0.000 |
| 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".