A Hybrid Framework for Short-Term Risk Assessment of Wind-Integrated Composite Power Systems
Bibliographic record
Abstract
This paper proposes a new framework for the short-term risk assessment of wind-integrated composite power systems via a combination of an analytical approach and a simulation technique. The proposed hybrid framework first employs the area risk method-an analytical approach, to include the detailed reliability models of different components of a power system. In this regard, a novel reliability modeling approach for wind generation for short-term risk assessment is also proposed. Thereafter, a non-sequential Monte-Carlo simulation technique is adopted to calculate the partial risks of the area risk method. As a result, the proposed framework is also capable of including the contingencies and constraints of the transmission system that are customarily neglected in the area risk method. The computational performance of the proposed framework is greatly enhanced by adopting the importance of sampling technique, whose parameters are obtained using the cross entropy optimization. Case studies performed on a modified 24-bus IEEE Reliability Test System validate that the detailed reliability modeling of wind generation and consideration of the transmission system are necessary to obtain more accurate short-term risk indices. Furthermore, the computational performance of the proposed framework is many orders higher than any other comparable methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".