Reduction of Cascading Outage Risk Based on Risk Gradient and Markovian Tree Search.
Bibliographic record
Abstract
Since cascading outages are major threats to power system operations, it is of great significance to reduce the risk of potential cascading outages. In this paper, a method for reduction of cascading outage risk based on Markovian tree (MT) search is proposed. Based on the MT expansion of the cascading outage risk, the risk gradient is computed with a forward-backward tree search scheme. The computation of risk gradient is incorporated into the procedure of risk assessment based on MT search, which is efficient with good convergence. Then the an optimization model for risk reduction (RR) is formulated by using risk gradient, which minimizes the cost of control while effectively reduces the cascading outage risk. Moreover, to overcome the limitation of linearization, an iterative risk reduction (IRR) algorithm is further developed. Test results on a 4-bus test system and the RTS-96 3-area test system verify the accuracy of risk gradient computation and effectiveness of the RR. And the performance of the IRR is demonstrated on RTS-96 3-area system and a 410-bus US-Canada northeast system model. The results show that the subsequent cascade risk and the total risk are reduced by 93.6% and 54.5%, respectively.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 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".