Genetic Algorithms: An Overview and Application to Power System Optimization
Bibliographic record
Abstract
Genetic Algorithm: An Overview and Application to Power System Optimization Steven M. Small, Benjamin Jeyasurya Faculty of Engineering & Applied Science Memorial University of Newfoundland St. John's, NL, Canada Abstract Power systems need to be operated in an economical and secure condition. Optimal Power Flow (OPF) has been widely used by Electric power utilities. The OPF optimizes a power system operating objective function (such as the operating cost of thermal resources) while satisfying a set of system operating constraints. In its most general formulation, the OPF is a nonlinear, nonconvex, large-scale optimization problem with both continuous and discrete control variables. OPF programs based on mathematical programming approaches are widely used. However, they are not guaranteed to converge to the global optimum. Recently Genetic Algorithms have been proposed as an alternative to solve the OPF problem. Genetic Algorithms are an attractive alternative to other optimization methods because of their robustness. The objective of this paper is to provide an overview of Genetic Algorithms as well as specific applications to power systems. Simple power system models will be used to illustrate the effectiveness of the Genetic Algorithm approach. Full paper to be considered for NECEC 2006
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".