Comparison of Crow Search and Practice Swarm Algorithm for Minimization of Losses in Unbalanced Radial Distribution System
Bibliographic record
Abstract
The crow search algorithm (CSA) was developed to optimize swarm intelligence by modeling crows' clever food concealment and retrieval.Simple structure, few tuning parameters, and easy implementation characterize the method.The crow search technique is used to frame an imbalanced radial distribution network in this research.This study aims to decrease power losses in uneven distribution networks via design.CSA strategies like power flow and DG placement decrease losses.A load-flow method for three-phase unbalanced radial distribution networks may easily incorporate these solutions into present networks.This approach optimizes network phase balance and conductor sizes.Planning goals include reducing total complex power imbalance, power loss, and average voltage drop.The thermal limit of each line and the minimum and maximum voltage limitations for each bus voltage confine the optimization.A three-phase forward-backward sweep load flow technique was developed to calculate these objective functions.The framing approach was evaluated on unbalanced radial distribution networks with 19 and IEEE 25 buses to determine its efficiency.Power loss and voltage drop are significantly reduced by optimizing phase balance and conductor sizes together.CSA outperformed and was more consistent than several meta-heuristic algorithms studied in this work.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".