Reconfiguration of distribution systems with distributed generators using Ant Colony Optimization and Harmony Search algorithms
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a study for the network reconfiguration problem for loss reduction with distributed generation units (DGs) included in the network. Two heuristic algorithms inspired from natural phenomena are proposed to solve the problem, real ants'-behavior-inspired, Ant Colony Optimization (ACO) implemented in the Hyper Cube (HC) framework and musicians'-behavior-inspired, Harmony Search (HS) algorithm. A 32-bus and 69-bus distribution systems were selected for optimizing the configuration with and without DG units installed. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study however, the HS required shorter simulation time but more practice of the iterative process than the HC-ACO. Implementing the ACO in the HC framework resulted in a more robust and easier handling of pheromone trails than the standard ACO. Insertion of DGs in the distribution networks contributed to loss reduction with reasonable percentage.
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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 | 0.000 |
| 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) | 0.000 | 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