Dynamic voltage balancing algorithm for modular multilevel converter with three-level flying capacitor submodules
Bibliographic record
Abstract
The modular multilevel converter (MMC) has several three-level flying capacitor (3L-FC) submodules in cascade. To balance the submodule capacitors voltage, this paper describes a dynamic voltage balancing algorithm based on the carrier phase shifted pulse width modulation (CPS-PWM) scheme. The submodules are controlled based on the instantaneous value of capacitor voltage and the direction of current. For the selection of the submodules, the maximum and minimum voltage submodule selection logics are designed. These voltage logics are generating an index number for each submodule based on the relative comparison of capacitors voltage. Finally the switching state of the submodules is generated by comparing the submodule index number with the dynamic reference index number. The performance of the proposed voltage balancing algorithm at different operating conditions is evaluated on 6kV/2MVA MMC system with the MATLAB simulation and the corresponding results are presented. In addition, the performance comparison of MMC with 3L-FC and conventional two-level half bridge (2L-HB) submodules is presented.
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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 | 0.001 | 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".