Capacitor Voltage Balancing and Current Control of a Five-Level Nested Neutral-Point-Clamped Converter
Bibliographic record
Abstract
The five-level nested neutral-point-clamped (5L-NNPC) converter is one of the most promising topologies for medium-voltage (2.3-7.2 kV) high-power applications such as medium-voltage drives, wind energy conversion systems, and grid-connected systems. The 5L-NNPC requires a fewer number of switching devices, freewheeling and clamping diodes, and flying capacitors compared to the existing five-level multilevel converters. In the 5L-NNPC topology, each flying capacitor voltage is regulated at one-fourth of the dc-bus voltage to obtain the five-level operation. Due to the lack of redundant switching states, it is difficult to control the flying capacitor voltages by using the pulse-width-modulation-based classical control methods. This paper proposes a model-predictive current control (MPCC) approach to control the flying capacitor voltages along with the output currents of the 5L-NNPC converter. The discrete-time model of 5L-NNPC is developed to implement the MPCC scheme. The simulation and experimental studies are conducted to verify the dynamic and steady-state performance of 5L-NNPC with the MPCC scheme. The performance of the proposed MPCC approach is compared with the conventional space-vector-modulation-based voltage-balancing approach. Furthermore, the flying capacitor voltage control capability of MPCC is verified at different load power factors.
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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 | 0.000 |
| 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".