A New Family of High-Frequency DC Link Two- and Multilevel NPC Inverters With Compact Design, Fast Dynamic, Enhanced Voltage Balancing, and Voltage Ripple Mitigation
Bibliographic record
Abstract
Traditional multilevel inverters (MLIs), such as neutral-point clamped (NPC) and active neutral-point clamped (ANPC) configurations, often rely on bulky capacitors, increasing system size and weight, which limits their application in space-constrained environments such as electric vehicles. This article introduces a novel two-level switched capacitor voltage doubler neutral-point-clamped (SC-VD NPC) inverter as a solution to these challenges. By utilizing high-frequency dc-links, the SC-VD NPC inverter significantly reduces the size of dc-link capacitors while providing a fast transient response. Its voltage balancing is enhanced through a sequential charging method and unique topology, offering advantages over conventional NPC and ANPC inverters, resulting in lower voltage ripple. A detailed mathematical analysis is presented to demonstrate these features. Furthermore, this configuration can be extended to the SC-VD NPC MLI, with three- and five-level topologies introduced, accompanied by a comprehensive comparison of cost, performance, and efficiency. Moreover, a novel one-dimensional space vector modulation (OD-SVM) scheme is introduced providing a simple and efficient method for controlling SC-VD NPC-MLIs. By utilizing voltage vectors to generate the desired reference voltage and selecting the closest vectors for dwell time determination, the OD-SVM streamlines the modulation process. Simulation and experimental results for two and five-level proposed MLIs validate these advantages.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".