A Series–Parallel Current-Driven Full-Bridge DC/DC Converter
Bibliographic record
Abstract
This paper presents a novel series-parallel current-driven (SPCD) full-bridge dc/dc converter, which is able to process and deliver power efficiently over a wide range of load variations. In order to guarantee reliable operation of high-frequency dc/dc converters, the converter should be able to sustain soft switching for a wide range of operating conditions. The SPCD full-bridge converter, proposed in this paper, is able to offer soft switching for the input power semiconductors and smooth commutations for the output diodes. Also, the particular structure of the proposed converter eliminates the need for extra auxiliary circuits to provide reactive current for soft switching at light loads. The proposed topology can fully eliminate voltage spikes across the output diodes by providing smooth and lossless commutations for the output diodes. Thus, the proposed converter can be an efficient and reliable solution for variety of applications with a high switching frequency and a high output voltage. The SPCD full-bridge converter has the ability to integrate all magnetic components into an integrated transformer in order to achieve a high power density. The integrated transformer is thoroughly analyzed using ANSYS high-frequency structure simulator. Simulation and experimental results confirm the superior performance of the proposed SPCD full-bridge dc/dc converter.
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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".