Clustering-Coefficient Based Resiliency Approach for Smart Grid
Bibliographic record
Abstract
Maintaining a power grid's functions is of paramount importance to grid operators as power is essential to almost all aspects of daily human activities. Grid resiliency keeps services up to the customers even upon failure incidences. A modernized power grid should balance the power load and overcome the fault state by minimizing its impact. Resiliency researchers have proposed many schemes for post-failure grid incidents. However, there is not yet an overall framework that combines both in-depth analyses of low-level topological characteristics to evaluate the existing grid resiliency and then improve it whenever required. In this paper, we present microlevel topological and resilience analysis of multiple IEEE bus systems. We propose a two-phase resilience framework for the smart power grid: the resiliency evaluation and the resiliency enhancement. While the first phase provides empirical evidence of the sufficiency of exploring a limited number of backup power lines upon an incident, the second phase employs clustering coefficient as a key indicator to enhance grid resiliency. We implement our proposed framework, validate it and show its effectiveness using the IEEE 14-bus 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 | 0.000 | 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 | 0.000 |
| 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".