Grey Wolf Optimizer for Optimal Distribution Network Reconfiguration
Bibliographic record
Abstract
The distribution network reconfiguration (DNR) has recently been brought to light as one of the most attractive strategies to enhance the performances of distribution systems. In this respect, this paper focuses on solving the DNR problem using a GWO (Grey Wolf Optimizer) algorithm. The proposed method was applied in an IEEE 69-bus test system to reduce its active power losses while satisfying the buses voltages, branches currents and radial topology constraints as well. To thoroughly assess the total active power losses of the distribution system, the Backward/Forward approach was developed in this study. Furthermore, the union-find with path compression technique was used to check the radiality constraint. So as to reveal its efficiency and suitability in solving the DNR issue and reaching the optimal solution, the proposed GWO algorithm was compared to the GA (Genetic Algorithm) and CF-PSO (Constriction Factor-Particle Swarm Optimization) as well. Moreover, it was validated against several techniques developed in recent literature. The research results disclosed that after performing reconfiguration, a significant reduction of total power losses evaluated at 56.17% was obtained and the voltage profile was generally improved.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".