MVO Algorithm for Optimal Simultaneous Integration of DG and DSTATCOM in Standard Radial Distribution Systems Based on Technical-Economic Indices
Bibliographic record
Abstract
Distributed Generation (DG) involving clean renewable energy resources and power electronic devices for control have been the main focus of researchers in electrical power engineering nowadays. The paper presents a new technique for obtaining the best locations and ratings of the DG units, which are based on photovoltaic solar panels, and the Distribution Static Compensator (DSTATCOM), in Radial Distribution Systems (RDSs). The objective function deployed is subject to equality and inequality constraints and aims to minimize three technical-economic system indices, which are Apparent Power Loss (APL), Total Voltage Variation (TVV), and Annual Losses Cost (ALC). Multi-Verse Optimizer (MVO) is a recently developed nature-inspired algorithm, which is utilized to obtain the optimal integration of DG and DSTATCOM into the system. In this paper, four case studies are considered, which involve the base-case, the individual deployment of either DG or DSTATCOM, and the simultaneous deployment of DG and DSTATCOM to test the system performance, while using the MVO algorithm. To verify its validity, the algorithm is tested on the standard IEEE 33- and 69-bus RDSs whose results are compared with the results obtained when using other existing algorithms. Comparison among results reflects the strength and suitability of the suggested MVO algorithm in minimizing the real power losses and enhancing the voltage profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".