A New Link Isolation Algorithm based on Complex Network Theory for Preventing Cascading Failures in Power Grid
Bibliographic record
Abstract
Weak links is one of the main causes of large-scale blackouts in power systems. How to accurately identify the weakest links so as to disconnect the links that could cause cascading failures when an accident happens to a power system, is a vital research topic for keeping power system safety by preventing large-scale blackouts caused by cascading failures. In this paper, based on complex network theory we first propose a method to identify the weakest links according to vertices second order centrality and links joint vulnerability metric by combining link betweenness and link load level, which is called joint vulnerability method (JVM). Based on JVM, we design a links isolation algorithm when a weakest link failure happens to a power system, which is called joint isolation algorithm (JIA). The simulation results show that JVM could identify the weakest links accurately, and the JIA could avoid large-scale blackouts effectively which plays a guiding role in the optimization of power system safety.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".