A hybrid controller for PWM active rectifiers based LCL filters
Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose a new hybrid control method of pulse width modulation (PWM) active rectifiers tied to the grid through an LCL filter. The control method is designed with the aim to achieve a perfect regulation of the dc-bus voltage; a near unity input power factor (UIPF) operation as well as a high quality of the line currents. Design/methodology/approach – The proposed hybrid control method consists of a PI-based linear controller cascaded with a nonlinear one. The nonlinear controller (inner loop) is designed using the input-output feedback linearization (IOFL) theory. It should control both the dc-bus voltage and the input currents at the converter’s poles. The linear controller (outer loop) is devoted to control the reactive line current so as to achieve a near UIPF. Findings – A perfect regulation of the dc-bus voltage and a near UIPF operation are achieved. Moreover, a high quality of the line currents is obtained. The robustness and effectiveness of the proposed control method have been successfully tested under variation of the dc voltage reference as well as grid and load disturbances. Practical implications – The proposed method is useful for single-stage and two-stage grid connected photovoltaic systems, wind energy conversion, and distributed power generation systems. Originality/value – The main novelty of this paper is the combination of linear and nonlinear controllers with the aim to control a PWM active rectifier tied to the grid through a third-order LCL filter. In the opinion, such control method has not been applied to this converter in earlier research papers. The numerical simulations carried out under normal and abnormal conditions confirm the effectiveness of the proposed approach.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".