New modulation scheme and voltage balancing control of modular multilevel converters for modern electric ships
Bibliographic record
Abstract
The distinctive features such as fault‐tolerability, modularity, power and voltage scalability have propelled modular multilevel converters (MMCs) as one of the emerging power converter topologies for marine applications. Pulse‐width modulation (PWM) schemes are widely used to control the MMCs due to their good control capability and ability to operate at higher switching frequencies. However, the required number of carriers for gating signal generation drastically increases with the submodule count per arm in an MMC. Moreover, these carriers should be accurately synchronised with each other to generate high power quality waveforms. However, it is hard to establish accurate synchronisation between these carriers due to the sampling issues, memory limitations, and computational delays. In addition, the MMC demands reconfiguration of PWM carriers and control strategy for voltage scalability and retrofit applications. To avoid these issues, a simple PWM scheme using one carrier is proposed in this study. The main advantage of the proposed scheme is an easy extension to the n ‐level MMC with a wide range of output voltage levels. The effectiveness of the proposed PWM scheme along with the capacitor voltage balancing control is demonstrated through detailed simulations and verified experimentally under steady‐state and dynamic conditions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".