Critical Links Identification for Selective Outages in Interdependent Power-Communication Networks
Bibliographic record
Abstract
Critical infrastructure, such as the smart grid, is vulnerable to failures and attacks. The complex nature of these systems embeds hidden vulnerabilities that threaten their functionality when exploited. In this paper, we perform a vulnerability analysis of the smart grid based on the power flow dynamics and in the presence of the essential communication network. Our analysis identifies a small number of power lines and communication links that can trigger a cascading failure and result in a blackout when removed. We quantify the failure effect in the form of fractional loss in the served load. Moreover, we formulate a mathematical model to present both components of the smart grid and their interdependency. A scalable algorithm is introduced to analyze the output of the model. We evaluate the proposed model and algorithm on the IEEE 14, 30, 57, and 300 Bus systems and associated communication networks, and report on the collected results.
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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 | 0.000 | 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 | 0.001 |
| 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".