A knowledge-based framework for power flow and optimal power flow analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes the application of formal methods for knowledge discovery from large quantity of data to reduce the complexity of Power Flow (PF) and Optimal Power Flow (OPF) problems. In particular, a knowledge-based paradigm for PF and OPF analyses is used to extract complex features, hidden relationships, and useful hypotheses potentially describing regularities in the problem solutions from operation data-sets. This is realized by designing a knowledge-extraction process based on Principal Components Analysis (PCA). The structural knowledge extracted by this process is then used to project the problem equations into a domain in which these equations can be solved more effectively. In this new domain, the cardinality of the PF and OPF problem is sensibly reduced and, consequently, the problem solutions can be obtained more efficiently. The effectiveness of the proposed framework is demonstrated with numerical results obtained for realistic power networks for several operating conditions.
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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