Model Predictive Control Strategy for Single-Phase Four-Cell Flying-Capacitor Totem-Pole PFC Converter with Integrated Power Pulsation Buffer
Bibliographic record
Abstract
This paper proposes an integrated power pulsation buffering (PPB) technique for a bidirectional single-phase fourcell flying capacitor multilevel (TP-4CFC) totem-pole power factor correction (PFC) converter. By leveraging the inherent energy storage of the flying capacitors, the architecture downsizes the required electrolytic DC-link capacitance and eliminates the need for separate decoupling circuits, enhancing system reliability and power density. A multi-objective model predictive current control (MOMPCC) strategy is employed to simultaneously regulate the grid current and flying capacitor voltages, effectively enabling partial PPB. A bang-bang-type voltage reference scheme based on instantaneous power deviation is used for capacitor voltage regulation. The proposed controller utilizes only lowfrequency inductor current and redundant switching states for PPB operation, simplifying hardware requirements. Simulation results demonstrate a significant reduction in DC-link voltage ripple by 53% and a Total Harmonic Distortion (THD) of 2.85% under integrated PPB operation, with a maximum switching frequency limited to 100 kHz. The presented solution is highly suitable for compact and long-lifetime applications such as EV onboard chargers and server power supplies.
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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) | 0.000 | 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) | 0.002 | 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".