Anti-Windup FOPID-Based DPC for SAPF Interconnected to a PV System Tuned Using PSO Algorithm
Bibliographic record
Abstract
This paper deals with a shunt active power filter (SAPF) integrated in a photovoltaic (PV) system, which is interfaced to the grid via a double-stage configuration, for simultaneously improving the power quality in the existence of non-linear loads and injecting the PV harvested power to the power grid. The direct power control (DPC) based on the conventional Proportional-integral (PI) suffers from some shortcomings in the transient state, such as large overshoots and undershoots in the voltage. Long response time is another disadvantage when using such a controller. To overcome this situation, the proposed control method is equipped by an anti windup fractional order proportional-integral differentiator (AW-FOPID) regulator, replacing the standard PI or PID regulators to maintain the DC link voltage at its desired value with small overshoots and undershoots in the voltage, while maintaining a short response time. The AW-FOPID controller, however, has five parameters, which makes it troublesome to tune. Accordingly, to adjust this AW-FOPID parameters, the Particle Swarm Optimization (PSO) algorithm is employed by minimizing the Integral Time Absolute Error (ITAE). Furthermore, an intelligent algorithm for tracking the maximum power point (MPPT) based on fuzzy logic has been applied to eventually resolve the drawback of the rapidly changing weather conditions. The overall control scheme is examined by simulation using MATLAB/Simulink software. The obtained simulation results and comparative study demonstrate the feasibility and performance of this control strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".