Optimal clustering for efficient computations of contingency effects in large regional power systems
Why this work is in the frame
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Bibliographic record
Abstract
The goal of this paper is to determine optimal clustering in large power networks for efficient contingency screening. A decentralized algorithm for “DC” contingency screening based on Diakoptics is revisited first. It has been shown that this algorithm is much more computationally efficient compared to the existing Distribution Factor Matrix methods for a pre-specified clustering. This paper will address how to establish the best clustering and quantify how much more efficient that clustering is compared to a pre-specified one. The optimality is defined in terms of computational complexity and necessary communication among the clusters. The optimal clustering requires the minimum balanced computational effort across the clusters with the minimum amount of information exchange. Optimal clustering will be illustrated on a sparsely connected RTS-96 bus system and a densely connected NPCC 36-bus system.
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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 it