A Bidirectional Current-Fed Isolated MMC With Partial Soft-Switching for High Step Ratio DC–DC Applications
Bibliographic record
Abstract
Medium-voltage direct current (MVDC) systems offer many advantages in a future of renewables and dc loads including reducing the number of required power conversion stages. An enabling technology for MVDC distribution systems will be high step ratio dc-dc converters with bidirectional power transfer capability, performing a role similar to ac distribution transformers. The current shaping modular multilevel converter (CS-MMC) has been proposed for this type of application. This converter family operates at high effective frequencies, enabled by the elimination of internal string inductors. While foundational work on the CS-MMC focused on unidirectional, voltage-fed operation, recent work has demonstrated the bidirectional capabilities of the CS-MMC when operated as a current-fed converter driven by the low-voltage side. This paper proposes an improved modulation for the bidirectional CS-MMC that (i) ensures zerovoltage switching (ZVS) of the secondary-side switches in forward operation and (ii) minimizes the reverse body diode conduction time to improve efficiency. This paper is also the first to detail a balancing mechanism between the VSM strings and between the dc-blocking capacitors of the bidirectional CS-MMC. Peak measured efficiencies of 97.0% in forward operation and 96.3% in reverse operation are achieved for a 6 module, 3 kW, 1kV/100V prototype
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".