Hybrid Model Predictive Control of Active-Neutral-Point-Clamped Multilevel Converters
Bibliographic record
Abstract
To reduce the power losses, the high-voltage switches in active-neutral-point-clamped (ANPC) multilevel converters always operates at the fundamental frequency. Moreover, DC-link capacitor and flying capacitor voltage balance control is mandatory in addition to output current control in the ANPC converters. Model predictive control (MPC) can provide this flexibility and feature high dynamics for transient operations. However, conventional MPC experiences variable switching frequency, high total harmonic distortion. Moreover, as there is no modulator in the conventional MPC, it is very hard to guarantee the fundamental-frequency operation of the high-voltage switches. Given the issues above, a hybrid MPC is proposed to control the ANPC converter in this paper, where finite-control-set MPC is utilized for high-voltage switch control, and duty-cycle-optimized MPC is utilized for the low-voltage switch control. The proposed MPC features a fixed switching frequency and improves the steady-state performance with simplified implementation. The validation of the proposed hybrid MPC strategy is conducted on a five-level ANPC converter.
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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".