A Centralized CB-MPC to Suppress Low-Frequency ZSCC in Modular Parallel Converters
Bibliographic record
Abstract
The parallel operation of three-phase converters has become an effective way to achieve modularity. However, zero-sequence circulating current (ZSCC) will appear when the multiparalleled converter modules share a common dc link. In the event that the converters have different output powers and inevitable circuit parameter mismatch, low-frequency (LF) ZSCC can be produced. To address the LF-ZSCC issues, in this article, we propose a centralized carrier-based model predictive control (CB-MPC) scheme for modular parallel converters. This control scheme can be implemented either in master converters or in a dedicate central controller. The CB-MPC can achieve both power control and LF-ZSCC elimination. In addition, with carriers adopted, interleaving and fixed device switching frequency can be easily realized. As such, the benefits of model predictive control and interleaving can be combined. Based on the ZSCC model derived in this article, the elimination of LF-ZSCC can be achieved for more than two paralleled converters when they have different output powers and/or with parameter mismatch. The effectiveness of the proposed CB-MPC has been verified by the simulation and experimental 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".