Hybrid Model Predictive Control of ANPC Converters With Decoupled Low-Frequency and High-Frequency Cells
Bibliographic record
Abstract
Active-neutral-point-clamped (ANPC) converters can be decomposed into a low-frequency cell (LFC) and a high-frequency cell (HFC), and fundamental-frequency operation of the LFC is becoming popular. However, balancing the dc-link voltage and three flying capacitor voltages while maintaining very fast control of the output current is always a challenging task, especially when the flying capacitor is small. This article proposes a hybrid model predictive control (MPC) to guarantee the fundamental-frequency operation of the LFC and achieve the fixed-switching frequency operation of the HFC. In the hybrid MPC, the classical MPC regulates the LFC, whereas the duty-cycle-optimized MPC regulates the HFC. The capacitor voltage balance is achieved by adjusting the duty cycles to avoid the weighting factor tuning process. The proposed MPC features a fixed switching frequency and improves the steady-state performance while guaranteeing the fundamental-frequency operation of the LFC. Simulation and experimental results on a five-level ANPC are presented to validate the advantages of the proposed hybrid MPC method over the classical MPC.
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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".