Parallel Capacitive-Link Universal Converters With Low Current Stress and High Efficiency
Bibliographic record
Abstract
Series capacitive-link universal converters are a relatively new class of single-stage power converters that offer numerous advantages including high reliability, high power density, and high efficiency. However, one of the limitations of these converters is high current stress of the switches, which lowers their efficiency especially in high current applications. In this article, a new class of soft-/hard-switched parallel capacitive-link converters is introduced to address this limitation of the series capacitive-link universal converter while keeping its advantages. Apart from providing bidirectional power transfer, the proposed ac–ac converter can implement voltage stepping up/down as well as frequency transformation. A small film capacitor, which is placed in parallel with the input and output switch bridges, is responsible for transferring the power from input towards output. A small inductor can be placed in series with the link capacitor to realize zero-current-switching for all the switches under any load conditions, which lowers the switching losses and reduces electromagnetic interference (EMI). The proposed parallel capacitive-link converter is expected to offer an enhanced efficiency and reduced current stress compared to the series capacitive-link universal converters. In this article, principles of the operation of the proposed converter are presented, and its performance and advantages are verified by simulation and experiment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".