Cascaded multilevel inverter using series connection of novel capacitor‐based units with minimum switch count
Bibliographic record
Abstract
This paper presents a new family of cascaded multilevel inverters (CMLIs) which can generate a considerable number of output voltage levels with minimum number of required accompanying switching devices. Conceptually, each stage of proposed CMLI is composed of using a novel capacitor‐based unit including two floating capacitors, one embedded dc voltage source and three power switches. In this case, the balanced voltage of integrated capacitors can be precisely provided as self‐voltage balancing without using any auxiliary circuits, close loop systems and intricate modulation techniques. In addition, to reach different number of output voltage levels, four different algorithms pertaining to the symmetrical, binary asymmetrical, trinary asymmetrical and also hybrid patterns for determining the magnitude of isolated dc voltage sources are presented. Hereby, proposed hybrid structure is capable of working under mixed switching frequency without aiming the conventional full H‐Bridge cell. Therefore, a high quality of output waveforms with reduced switching devices as well as power loss dissipation can be alternatively achieved. To confirm the validity of proposed CMLI, a complete comparison with several recently presented topologies besides several simulation and experimental results based on trinary asymmetrical and hybrid evolved structures will be also given.
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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 | 0.000 | 0.001 |
| 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".