On the effects of distributed control area design for the stabilization of cyber-enabled smart grids
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Bibliographic record
Abstract
We study the effect of control area design on the performance of distributed control. Specifically, we consider distributed implementations of parametric feedback linearization (PFL) control for efficient transient stability after the occurrence of a power system disturbance. We employ hierarchical spectral clustering and k-mean spectral clustering techniques to design control areas with high physical coupling within the power system. We address three distributed control scenarios: (i) distributed control applied to all generators of a control area, (ii) distributed control applied only to the largest inertia generator within a control area, and (iii) hierarchical distributed control where all generators apply distributed control and lead generators within a control area have centralized control. We investigate the effect of area clustering outcomes and compare the performance of the three control approaches for various power system faults.
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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 | 0.001 | 0.001 |
| 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