Model Predictive Control of a Three-Phase Seven-Level Nested Switched-Capacitor Converter with Flying Capacitor Voltage Balancing
Bibliographic record
Abstract
This paper presents a three-phase seven-level nested switched-capacitor (7L-NSC) inverter that incorporates two symmetrical flying capacitors per phase, each rated at one-third of the DC-link voltage. To ensure reliable operation across all conditions, a finite control set model predictive control (FCS-MPC) strategy is employed. This control approach leverages redundant switching states to maintain accurate current tracking and ensure effective voltage balancing of the flying capacitors. A detailed mathematical analysis of the inverter, including capacitor voltage ripple behavior under both steady-state and transient conditions, confirms the robustness of the proposed control method. The performance of the control strategy is validated through comprehensive simulation studies in PSIM and verified by experimental testing using a laboratory-built prototype. Results from both simulations and hardware experiments demonstrate the method’s capability to maintain low THD and stable capacitor voltages, confirming its suitability for advanced multilevel inverter applications.
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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) | 0.000 | 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".