Distribution Feeder Reconfiguration with Distributed Generation Using Backward/Forward Sweep Power Flow – Grey Wolf Optimizer
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Bibliographic record
Abstract
This article presents an effective combination method based on Backward/Forward Sweep Power Flow- Grey Wolf optimizer (BFSPF-GWO) for feeder reconfiguration in a distribution network with the presence of distributed generation (DG). The 33-bus test system by adding five tie line switches is proposed with the objective functions of minimizing total power losses and improving the voltage profiles. The results reveal a reduction in active and reactive power losses at 71.41% and 67.66%, respectively. The optimal sizing of DG and installation location are identified by installing a 2.26 MW DG at bus 29. The magnitudes of voltage profiles and critical buses in the test system have been improved. The proposed BFSPF-GWO algorithm’s performance in DG placement and sizing with feeder reconfiguration has been evaluated by comparing the results with Mixed-integer optimization by GA (MIOGA) and Particle Swarm optimization (PSO).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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