Model Predictive Control of 5L-ANPC Converter-Fed PMSM Drives with Two-Stage Optimization
Bibliographic record
Abstract
Classical model predictive control (MPC) of a five-level active neutral-point-clamped (5L-ANPC) converter-fed PMSM drive faces two limitations: heavy computation burden and poor steady-state performance. This paper proposed an MPC scheme with two-stage optimization to reduce the computational burden and improve steady-state performance. To simplify the computational complexity, the 5L-ANPC converter is decoupled into two parts: the low-frequency cell (LFC) and the high-frequency cell (HFC). In the first stage, the switching states of the LFC are selected based on the sign of the desired output voltage; in the second stage, the optimal duty cycles for the high-frequency cell (HFC) are calculated by the multiple vector MPC. The pulse train for each switching state is generated by phase-shifted pulse-width modulation (PS-PWM) based on the voltage-second balance principle. The phase capacitor and DClink capacitor voltage balance are achieved by the flexibility of the inherent redundancy in the 5L-ANPC converter. An efficient optimization method is also formulated to reduce the classical enumeration algorithm to only 6 times, which significantly simplifies the computational burden of MPC. Due to the interleaved switching manner within each phase, the steady-state performance compared with well the linear controller with PSPWM. Experimental evaluations have been presented to validate the effectiveness of the proposed 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 | 0.000 |
| 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".