A Bidirectional Multiple-Input Multiple-Output Modular Multilevel DC–DC Converter and its Control Design
Bibliographic record
Abstract
This paper introduces a multiple-input multiple-output modular multilevel dc-dc converter (MIMO-MMC) and its associated control scheme. The proposed topology has a bidirectional structure and may be utilized in both low- and high-power applications ranging from approximately 100 W-10 MW. The modular structure of the MIMO-MMC enables efficient component utilization through module voltage and current sharing capabilities. The topology can supply or extract regulated power from an arbitrary number of controllable voltage nodes without requiring source or load multiplexing, resulting in minimized filtering requirements. The MIMO-MMC's structure is described, and the steady-state operation of the converter is theoretically analyzed. The dynamic models of the converter for both step-down and step-up configuration are derived and employed to devise an effective control algorithm for closed-loop operation. A general method is provided for stability analysis of the proposed closed-loop system followed by a case study verifying stability of the system at different operating points. The steady-state operation and dynamic response of the converter under both configurations is investigated through simulation and experiment.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".