Optimized PI tuning of DG-Integrated Shunt Active Power Filter Using Biogeography-Based Optimization Algorithm
Bibliographic record
Abstract
This article proposes a biogeography-based optimization (BBO) approach to optimizing the PI controller gains of the PV integrated shunt active power filter (SAPF).This method is also used to analyze how these optimum gains affect the performance of SAPF.When nonlinear loads draw reactive power from the source, harmonics are produced, which can lead to power quality issues for the utility.Our ultimate goal is to achieve sinusoidal source current, which can only be accomplished with the help of FACT devices that enhance power quality in the distribution network.SAPF is a commonly utilized device.Solar is the most popular alternative energy source because it is the most cost-effective.Therefore, this article takes into account PV-integrated SAPF for analysis.To get the highest amount of power from a PV panel, a technique called maximum power point tracking (MPPT) is used.This technique is based on perturbation and observation (P&O) in MPPT.When the SAPF converter uses this maximum power, it also sends active power to the load, so that the SAPF converter has dual functionality like reactive power compensation (FACT) and active power supply (DG Functionality) to the load.The performance of this PV-Integrated SAPF depends on many things, like how the reference current is made, how switching pulses are made, how the voltage on the dc link is controlled, etc.In order to obtain the best possible performance from SAPF, certain design parameters, such as the gains of the PI-controller, need to be optimized.Modeling and simulation of a PV-integrated SAPF are performed in Matlab/Simulink.Gain optimization of the PI controller-based PV integrated SAPF is performed using both the proposed biogeography-based optimization method and the PSO algorithm, and the results are compared.The proposed BBO-trained PV-SAPF converter's active power injection was also investigated.
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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) | 0.001 | 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) | 0.002 | 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".