Grey Wolf Optimizer for Optimal Distribution Network Reconfiguration
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it