Vector Shifted Model Predictive Power Control of Three-Level Neutral-Point-Clamped Rectifiers
Bibliographic record
Abstract
Model predictive power control (MPPC) has been emerging as one of the most promising control schemes for three-level neutral-point-clamped (NPC) rectifiers. However, conventional MPPC (C-MPPC), which only selects one switching state during the entire sampling period, leads to high active and reactive power ripples. Moreover, the heavy computational burden and variable switching frequency limit the applications of MPPC. In this article, vector shifted MPPC (VS-MPPC) methods are investigated. With the shifted vectors, the constant-switching-frequency MPPC of three-level NPC rectifiers can be simplified as that of a two-level rectifier, and the balanced neutral capacitor voltage can be easily achieved by adjusting the duty cycle of the redundant switches without any weighting factor employed. Only eight voltage vectors are calculated and shifted based on the small hexagon selection. Consequently, the computational burden is significantly reduced, even 35% less than that of C-MPPC. Furthermore, the proposed VS-MPPC presents a constant switching frequency and better steady-state control performance without evaluating all the switching states. Simulation and experimental evaluations of the proposed VS-MPPC methods with C-MPPC have been conducted to validate the superiority of the proposed VS-MPPC methods.
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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 | 0.001 |
| 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 | 0.001 | 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".