Electric Vehicle to Power Grid Integration Using Three-Phase Three-Level AC/DC Converter and PI-Fuzzy Controller
Bibliographic record
Abstract
This paper presents the control and simulation of an electric vehicle (EV) charging station using a three-level converter on the grid-side as well as on the EV-side. The charging station control schemes with three-level AC/DC power conversion and a bidirectional DC/DC charging regulator are described. The integration of EVs to the power grid provides an improvement of the grid reliability and stability. EVs are considered an asset to the smart grid to optimize effective performance economically and environmentally under various operation conditions, and more significantly to sustain the resiliency of the grid in the case of emergency conditions and disturbance events. The three-level grid side converter (GSC) can participate in the reactive power support or grid voltage control at the grid interfacing point or the common coupling point (PCC). A fuzzy logic proportional integral (FL-PI) controller is proposed to control the GSC converter. The controllers used are verified and tested by simulation to evaluate their performance using MATLAB/SIMULINK. The comparison of a PI-controller and a PI-Fuzzy controller for the EV charging station shows the effectiveness of the proposed FL-PI controller over conventional PI controller for same circuit operating conditions. A good performance for PI-Fuzzy in terms of settling time and peak overshoot can observed from the simulation results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".