A Novel Discrete Particle Swarm Optimization Algorithm for Optimal Capacitor Placement and Sizing
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.002 | 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".