Switched Capacitor Integrated (2<i>n</i> + 1)-Level Step-Up Single-Phase Inverter
Bibliographic record
Abstract
This article presents a novel switched capacitor (SC) based (2n + 1)-level single-phase inverter with a reduced number of components and input dc voltage supply. This inverter is designed in a way that just one dc source is required to generate different voltage levels. The circuit consists of three major parts, i.e., front-end boost stage, active SC cell(s) in the middle, and H-bridge inverter at the end. The total number of output voltage levels is up to (2n + 1) levels, where n ≥ 2 is the number of switching cells, which consists of three active switches and two capacitors. Compared with conventional SC-based multilevel inverter topologies, the proposed topology features many advantages, such as low number of semiconductor devices, quasi-resonant charging of capacitors that reduce the inrush current and current stress on the devices, self-balancing of capacitor, and reduced voltage stress on the switches. Moreover, a simple sinusoidal pulsewidth modulation technique is employed here to generate the modulation signals for the proposed inverter. The operating principle is presented in detail followed by comparative analysis, thermal modeling, and design guidelines. Finally, computer simulation and laboratory test results are carried out for a five-level inverter with one SC cells as well as a seven-level inverter with two SC cells as two examples to verify the performance of the proposed (2n + 1)-level inverter. Measurement results show that the proposed inverter has the 96.5 ± 1% efficiency over a wide range of load with a peak efficiency of 98.56%.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".