Family of soft‐switching pulse‐width modulation converters using coupled passive snubber
Bibliographic record
Abstract
Efficiency, power density and electromagnetic interference (EMI) stand among the main concerns in power electronics and determine the quality of a power converter. To address the aforementioned concerns, this study proposes a cost effective passive soft‐switching technique using coupled inductor for pulse‐width modulation (PWM) converters. Through providing soft‐switching conditions, switching losses are reduced and the use of coupled inductor technique improves the power density. Since, the slope of voltage and current variations over time is reduced by the proposed passive soft‐switching technique, the EMI level is expected to reduce. This technique can be applied to a wide range of PWM converters, however, the analysis is focused on a soft‐switching boost converter to provide a framework. The experimental measurements of the realised 200 W boost converter show that the efficiency is improved by 2 and 3.8% as compared to a hard switching boost converter with and without an RCD snubber circuit, respectively. Moreover, the experimental EMI measurements indicate that with no external EMI filter, the proposed technique has reduced the main peak of EMI level by 8 dBµV (in comparison to its hard switching counterpart) which satisfies CISPR22 class A EMC standard limitation.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".