Device Loading and Reliability Analysis of Modular Multilevel Converters With Circulating Current Control and Common-Mode Voltage Injection
Bibliographic record
Abstract
The submodule (SM) capacitor voltage ripple and circulating current (CC) are major technical challenges in modular multilevel converters (MMCs). The generation of CC in the MMC results in an increase of SM capacitor voltage ripple, voltage/current stress on power switching devices, and power losses. Therefore, minimizing the CC and SM capacitor voltage ripple is essential for the smooth operation of an MMC. Prior studies and research have focused on different control strategies and modulation schemes to minimize the CC and SM capacitor voltage ripple. This paper presents an impact of active CC control and common-mode voltage (CMV) injection on thermal loading of power switching devices. To evaluate the performance comparison between different control approaches, a generalized control framework for MMC is established. An electrothermal simulation is performed to study the thermal loading on power switching devices. The efficacy of the generalized control scheme is evaluated through PLECS simulations and hardware-in-the-loop experiments. Finally, the thermal loading and reliability of MMCs are evaluated for different modulation schemes with the active CC control and CMV injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".