Optimization of Proportional Resonant and Proportional Integral Controls Using Particle Swarm Optimization Technique for PV Grid Tied Inverter
Bibliographic record
Abstract
This article outlines how to optimize the parameters of proportional integral (PI) and proportional resonant (PR) controllers of a grid connected three-phase inverter system using Particle Swarm Optimization (PSO).The optimization of the control parameters is constrained to a low total harmonic distortion (THD) in the injected currents to the grid.In contrast to conventional PI and PR tuning methods, the PSO optimized controller inherits a wide-ranging operating condition, having better performance in steady-state and rapid dynamic response under abnormal grid conditions.This approach is used to provide the simulation results under both abnormal and normal grid conditions.Some important characteristics of this technique are that its complexity is not altered, no additional hardware is required, and no additional cost is added.From the presented results, it is observed that the proposed control optimization method ensured a low THD of 0.59% in the injected grid currents.Moreover, the control parameter optimization error converges to zero in finite time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".