PSO-Based PI Controller for Voltage-Oriented Controller based Vienna Rectifier for Electric Vehicle Charging Stations
Bibliographic record
Abstract
Nonlinear processes are extremely frequent in the process of the industry, and it is always better to build a stabilizing controller to optimize the rate of output. One of the recent emerging technology which makes the system nonlinear is electric vehicle charging stations. This research work aims to optimize the PI controller with a voltage-oriented controller-based Vienna rectifier for electric vehicle charging stations (EVCS). With the trial and error method for EV charging stations, the PI controller parameters K_p and K_i values, the error values such as integral square error (ISE) and integral absolute error (IAE), system parameters such as rise time settling time, and peak overshoot values are extremely high in the Vienna rectifier based VOC system. Due to the increased system parameters, the stability of the system has been reduced, which approaches a reduction in the system efficiency. The system parameters must be mitigated to provide stable operation to overcome the aforementioned issues with EV charging stations. In this research work, the PSO optimization-based PI controller for EV charging stations has been proposed. The proposed PSO-PI controller optimizes the control parameters and reduces the error values to make the system more stable. The proposed system is simulated and optimized using MATLAB software. The proposed PSO-PI controller-based EV charging station improves the system stability 12% more than the existing VOC controller with Vienna rectifier for EV charging stations.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".