A New Approach for Contingency Analysis Based on Centrality Measures
Bibliographic record
Abstract
The contingency analysis of power systems represents a critical part of security monitoring, required to maintain power system reliability. In general, traditional N-1 contingency analysis methods simulate a few outages, due to the high computational costs involved. Thus, they may fail to identify some critical contingencies that can lead to cascading failures. This paper proposes a new approach to N-1 contingency analysis of electric transmission systems, based on network centrality measures. The proposed method evaluates all possible transmission line outages in a very short computational time, and it requires only topological information. Results are shown for two electric power systems: ITAIPU 11 bus and IEEE 39 bus. Comparisons between the results obtained by the proposed method and traditional ones show the accuracy of the proposed method to identify critical buses and transmission lines, in local and global context, even in absence of electrical information. The proposed method is interesting as a direct and fast tool applied to the pre-analysis process, since topological network behavior is verified. Thus, pre-analysis provides a prior response to the system operator of the points by which the electrical power system analysis should begin, in order to ensure safe operational state of the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".