Methods for Vulnerability Analysis of Power Systems
Bibliographic record
Abstract
Power systems are a vital infrastructure for the functioning of the society, generally regarded as one of the most critical infrastructures. Past power system blackouts (e.g., the Canadian ice-storm and New-Zeeland Power outage in 1998, the U.S. blackout in 2003, the European blackout in 2006 and the Indian blackout in 2012) have revealed the inherent vulnerabilities of power systems as well as the catastrophic consequences of major power supply disruptions. Hence it is of utmost importance to ensure both reliable and robust power supply to the society. Here it is argued that methods for vulnerability analysis complements traditional reliability oriented methods. The aim of the paper is to, through a numerical example, more readily discuss how the presented vulnerability oriented methods can be used in a decision context and how the results can be used from a system operator perspective, as such a topic is to a large extent lacking in current research literature. Three types of vulnerability analyses, addressing structural and geographical vulnerabilities, are demonstrated by using the IEEE RTS96 transmission system. The conclusion is that the presented vulnerability methods can give guidance towards what types of hazards and threats the system is vulnerable to and give guidance in decisions of how to decrease the vulnerability of the system.
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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 | 0.001 | 0.004 |
| 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".