Locating and Sizing of Distributed Generation Sources and Parallel Capacitors Using Multiple Objective Particle Swarm Optimization Algorithm
Bibliographic record
Abstract
In this paper, the MOPSO algorithm has been used to locate and determine the capacity of distributed generation sources and capacitor banks in the distribution system. The intended objective function is a combination of different objective functions. The first goal is to reduce losses, and the second goal is to improve the voltage profile and the third goal is to reduce costs, which has been used by placing weight coefficients in the form of an objective function in the algorithms. For this purpose, the standard 33-bus system has been used to conduct studies. Studies have been repeated in three scenarios. In the first scenario, the locating and determination of the capacity of active and reactive resources has been accomplished with the approach of reducing losses and improving the voltage profile. However, in the second scenario, the locating and determining the capacity of these resources has been accomplished with loss and cost reduction approach and it was considered as constraint in voltage profile. In the third scenario, the simultaneous reduction of all three objective functions has been performed simultaneously. To validate the results obtained by the MOPSO algorithm, its results were compared with genetic and particle swarm algorithms. The results indicate better and more accurate performance of MOPSO algorithm in minimizing objective functions relative to other two algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".