A knowledge-based framework for power flow and optimal power flow analyses
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".