Transmission grid vulnerability assessment by eigen-sensitivity and cut-set screening
Bibliographic record
Abstract
This paper deals with the assessment of the vulnerability of the transmission grid under extreme contingencies, including a terrorist threat or an exceptional natural disaster. It proposes a two-step screening-and-ranking approach to find the dynamically most disruptive disturbances created by multiple-line outages. In the screening step, critical transmission lines are selected according to the sensitivities of the critical system eigenvalues to the loss of transmission lines, complemented by a topology analysis that searches for the cut-sets in the system leading to islanding. In the ranking step, time-domain simulations are performed for the contingencies given by the combination of lines screened out in the first step in order to determine and classify their actual dynamic impacts. Results obtained from a test system show that the proposed approach is able to screen out most of the critical multiple-line outages in terms of load shedding and transient stability.
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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 | 0.000 |
| 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".