Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an Imperialist Competitive Algorithm (ICA) for optimal multiple distributed generations (DGs) placement and sizing in a distribution system. The objective is to minimize the total real power losses and improve the voltage profile within real and reactive power generation and voltage limits. Three types of DG are considered and the ICA is used to find the better sizes and locations of DGs for maximum real power losses reduction and voltage improvement for given number of DG units in each type. Both integer and continuous variables are considered in ICA, integer variable for locations and continues variable for sizes. The total real power losses and voltage profile evaluation are based on a power flow method for radial distribution system with the representation of DGs. The proposed method has been demonstrated on 33 bus radial distribution system. The efficiency of the ICA in reducing the total power losses and improving voltage is validated by comparing the obtained results with Particle Swarm Optimization (PSO) algorithm.
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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 | 0.001 | 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) | 0.000 | 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