A power quality‐based planning framework for flicker minimization of wind turbines in distribution network
Bibliographic record
Abstract
Abstract Renewable energy penetration in distribution networks, especially wind turbines (WTs) and photovoltaics (PVs), leads to an increase in power quality disturbances. One of the most important power quality issues is flicker produced by WTs. Here, to mitigate the flicker produced by WTs, distribution network planning (DNP) problem is solved concerning flicker minimization. For this aim, a weighted objective function is defined in which in addition to power losses, flicker emission is also considered. In the planning problem, siting and sizing of WTs as well as siting of PVs are investigated to simultaneously minimize power losses and flicker emission. Owing to the difficulty of this optimization problem, a new variant of genetic algorithm (GA), named elitist‐based GA (EGA), is proposed whose crossover operator works based on the elite chromosome. This variant provides a promising way to efficiently use information from the best solution to generate new solutions. Simulation results show that optimal siting and sizing of WTs can considerably improve the network parameters in terms of power losses and flicker emission. Moreover, simulation results show the efficiency and effectiveness of EGA compared to the other studied techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".