A Photovoltaic System Controlled by the Particle Swarm Optimization Algorithm Supplied a Shunt Active Power Filter
Bibliographic record
Abstract
The distortion of the currents caused by non linear loads gives rise to harmonics that shorten the life of the devices and damage the electrical grid, causing malfunctions and overheating. The distortion of the currents caused by non linear loads gives rise to harmonics that shorten the life of the devices and damage the electrical grid, causing malfunctions and overheating. Mitigation of harmonics problems and reactive power compensation are necessary in order to improve the Total Harmonic Distortion and increase the power factor. The Shunt Active Power Filter (SAPF) reduces harmonics and greatly improves the sinusoidal shape of the current. This paper presents an application of the photovoltaic system that envelops photovoltaic energy source and DC-DC boost converter, for the control of the latter, we used the MPPT technique based on the Particle Swarm Optimization (PSO) to supply a two-level inverter applied to a SAPF based on three phases connected to the grid. The obtained results obtained with MATLAB/Simulink show clearly a good performance of the SAPF with the integration of the proposed work. Again, these results are compatible with those required by the electrical network and which follow the international standard recommendation IEEE-519 1992.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".