Fully Soft-Switched Non-Isolated High Step-Down DC–DC Converter With Reduced Voltage Stress and Expanding Capability
Bibliographic record
Abstract
In this article, a fully soft-switched expandable high step-down dc–dc converter is presented. In order to achieve a high step-down voltage conversion ratio and low component count, the synchronous buck and Cuk converters are integrated. Lower switches’ voltage stress is realized by splitting the input voltage, which can considerably decrease the switches’ conduction loss. Moreover, switching losses along with the reverse recovery problems are mitigated due to the soft-switching operation of all semiconductor devices. The switch utilized for soft-switching operation is also used to replace the output diode as a synchronous rectifier, which reduces the conduction loss. The mentioned features have considerably contributed to the converter efficiency. Furthermore, the converter shares a common ground between the input and the output, which is desirable in many applications, while the converter output current is continuous without adding current ripple cancellation methods. Finally, the number of converter cells can be expanded or reduced; therefore, the converter can be applied to a wide range of loads. The operating principles and analysis of the proposed converter are presented, and the results from the implemented prototype are provided to verify the converter operation and performance.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".