LSF Integrated Hippopotamus Optimizer Algorithm for Single DG Optimization in Radial Distribution Power Network
Bibliographic record
Abstract
Distributed generation (DG) allocation provides significant benefits to radial distribution power networks (RDPN) when its size and location are optimally determined. This paper proposes a novel and efficient integrated approach for optimizing the site and rating of a single DG unit to minimize the total real power losses (TRPL) of the RDPN. The proposed method combines the loss sensitivity factor (LSF) with an advanced metaheuristic technique, the Hippopotamus Optimizer Algorithm (HOA). The LSF is first computed to identify a set of potential locations for DG placement, while HOA leverages the unique defense mechanisms and evasion strategies of hippopotamuses to optimize the DG capacity. The effectiveness of the proposed integrated approach is validated on the balanced IEEE 33-bus benchmark RDPN under both nominal and peak load conditions. To further assess robustness, type I and type III DG unit placements are considered.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".