A Novel Discrete Particle Swarm Optimization Algorithm for Optimal Capacitor Placement and Sizing
Why this work is in the frame
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Bibliographic record
Abstract
Voltage profiles throughout the electric power system network have to be kept at acceptable levels to ensure network reliability among other issues. Capacitor banks are commonly installed in various parts of the electric grid to maintain voltage levels within proper limits. In general, feeders in distribution systems include the majority of shunt capacitors installations to boost up voltage levels. In this paper, a novel approach is proposed to optimally solve the problem of determining the location and size of shunt capacitors in distribution systems. Traditionally, the problem is usually solved in two steps; first by determining the location of the "needed" bus and then selecting the proper size. The proposed method solves the problems of finding the optimal capacitor size and location simultaneously. Throughout the optimization process, both the capacitor injected reactive power and its location are being treated as discrete variables. The objective function considered in this paper is to minimize the total feeder losses. The proposed algorithm was tested on a standard test system. Results signify the robustness of the proposed algorithm in solving this difficult integer programming problem.
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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 | 0.000 |
| 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