Optimization of Fractional Order PI Controller by PSO Algorithm Applied to a Grid-Connected Photovoltaic System
Bibliographic record
Abstract
With the increasing integration of renewable energies into power grids, their control and power quality are becoming the main focus of many research efforts. In a grid-connected photovoltaic system, the control strategy is necessary to efficiently use the solar energy as well as to ensure high power quality. This paper presents a study on the robustness of a Fractional Order PI controller based on the Particle Swarm Optimization algorithm (PSO-FOPI) in a grid-connected PV system. The controller used was integrated into the inverter to apply voltage-oriented control (VOC). Fractional order controllers have an additional degree of freedom, so that a wider range of parameters is available to provide better control. Parameter optimization of the FOPI and classical PI controllers are performed using the PSO algorithm. The performance of the FOPI controller is compared with that of the classical PI controller. A complete study of the behavior of the grid connected PV system is tested using MATLAB/Simulink. The simulation results show the performance and efficiency of the PSO-FOPI controller compared to the classical PI controller in terms of rapidity, stability and precision, as well as the THD reduction of the current injected to the grid for any variation of solar irradiance.
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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 | 0.001 | 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".