Optimal Integration of Renewable Distributed Generation in Practical Distribution Grids based on Moth-Flame optimization Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Distributed generator (DG) resources are small scale electric power generating plants that can provide power in distribution grids. The above benefits can be achieved by optimal integration of DG using novel optimization algorithm namely Moth-flame optimization (MFO) algorithm for determine the optimal location and sizing to reduce the power losses and augmented voltage stability index. The proposed algorithm is evaluated on IEEE 69-bus, and practical radial distribution grids: Constantine City 73-bus and Indian 85-bus. The installed DGs are photovoltaic (PV) and wind turbine (WT) sources. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the MFO algorithm. The validity of the proposed MFO algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it