Indirect Control of Capacitor Voltage Ripple and Circulating Current in a Modular Multilevel Converter
Bibliographic record
Abstract
Model predictive control is a promising approach to control a multi-objective modular multilevel converter (MMC). In this approach, the control objectives of MMC are included in a single cost function and evaluated for all possible switching states by using weighting factors. The complexity of weighting factor selection process increases with the number of control objectives, and it affects the performance of MPC as well. To reduce the dependency on the weighting factors, a new model predictive control with zero-sequence voltage injection is proposed. With the proposed approach, some of the control objectives like reduction of submodule capacitor voltage ripple and circulating current can be achieved without using a cost function. The proposed approach also reduces the total harmonic distortion of the output voltage and current waveforms. The performance comparison of the proposed and existing MPC approach has been verified through MATLAB simulations on a three-level flying capacitor (3L-FC) based MMC system.
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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) | 0.001 | 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".