Multi-objective hunter prey optimizer technique for distributed generation placement
Bibliographic record
Abstract
Accommodation of distributed generation (DG) units in the distribution power network (DPN) reduces the power losses (PL), improves the voltage profile (VP), and enhances the stability. The size and site for distribution generations have to be optimized to avail favorable results. Otherwise, the DPN may experience greater power losses, higher voltage deviation, and voltage instability issues. This article implements an optimization technique using a hunter-prey optimizer (HPO) algorithm to optimize single and multiple (two) DG units in the radial DPN to minimize total real power losses (RPL) and total voltage deviation (TVD). The effectiveness of the HPO algorithm is assessed on the IEEE benchmark 69-bus radial DPN and a real-world Cairo-59 bus RDS. The simulation outcome after the optimized inclusion of DGs shown significant RPL reduction and considerable voltage enhancement. Furthermore, the optimized results of HPO algorithm were compared to the different algorithms and the comparison proved that the HPO can provide a more promising and authentic outcome than other algorithms.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".