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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".