Dual-stage model predictive control of modular multilevel converter
Bibliographic record
Abstract
The control of modular multilevel converter (MMC) is quite challenging and demands a flexible approach to achieve multiple control objectives. The model predictive control (MPC) is highly effective and suitable to control the MMC. The control objectives are included in a single cost function and evaluated for all possible switching states. The switching state which minimizes cost function is selected and applied to converter. In MMC, the number of available switching states are quite high and drastically increase with number of submodules per arm. Therefore, the implementation of conventional MPC is difficult and contributes to unwanted switching of submodules. This paper proposes a less computational, dual-stage MPC with common-mode voltage injection for MMC. In this approach, the overall MMC control objectives are categorized into primary and secondary group. The primary group objectives are evaluated in first-stage and secondary group of control objectives are evaluated in second-stage of MPC. By doing so, the computational complexity can be significantly minimized without affecting the dynamic response of MPC. The proposed approach avoids unwanted switching of submodules and, minimizes the output voltage harmonic distortion and ripple in output current. The simulation study is conducted to verify the effectiveness of proposed approach and corresponding results are compared with standard MPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".