Modulated Model Predictive Control for Grid-Connected Current Source Converter With LC Resonance Suppression
Bibliographic record
Abstract
This article proposes a modulated model predictive control (MPC) with finite-control-set (FCS) to tackle the LC resonance problem in grid-connected current source converter (CSC). The proposed scheme pre-calculates the duty cycle of two active current vectors and the zero current vector for three virtual vectors, and then a cost function, in which the capacitor voltage feedback is introduced besides the grid current error penalization, is designed to select the optimized modulated vector. Therefore, the FCS reduces to only three virtual vectors during each predictive horizon with low computational burden. The proposed method not only realizes the optimization process including vectors selection and duty cycle determination simultaneously, but also can achieve superior steady-state performance and well dynamic response without LC resonance. Constant switching frequency can be realized via embedding space vector modulation (SVM) into the MPC algorithm. Capacitor voltage is estimated by an observer instead of using transducers to save hardware cost. Simulated and experimental results have verified the correctness and effectiveness of the proposed method.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".