Hybrid Method Based on Metaheuristics and Interior Point for Optimal Power Flow
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we present a hybrid algorithm based on metaheuristics and the interior point (IP) method from MATPOWER to solve the optimal power flow problem. The control variables optimized are the real power and voltage of the generators, the transformer tap ratios and angles and the settings of the static volt-ampere reactive compensators (SVARs). The metaheuristic is used to optimize the discrete variables while MATPOWER is used at every evaluation of the fitness function to compute optimized values for the continuous variables. Compared to methods relying only on metaheuristics, our proposed approach is able to optimize the control settings for networks that are much larger. Compared to using MATPOWER alone, our proposed approach is able to optimize the transformer and the SVAR settings. To select the metaheuristic that is best suited for this application, five metaheuristics were implemented and compared. The software was implemented in MATLAB and parallelized to run on a computer cluster. The proposed algorithm was tested on networks up to 2383 buses.
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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