An improved PDM control method for a high frequency quasi-resonat converter
Bibliographic record
Abstract
This paper proposes a Pulse Density Modulation (PDM) method with improved transient for high frequency converters such as Class E DC/DC or quasi-resonant boost converters. In contrast to frequency or duty cycle control, the conventional PDM control method provides high efficiency at any load when modulation frequency is low. However, efficiency drops significantly for higher modulation frequencies due to the transient power losses in each power pulse. The proposed method decreases the power losses at the beginning of each pulse and reduces the voltage stress of the switch. Therefore, a higher modulation frequency and smaller filter size become feasible. Startup behavior of a quasi-resonant boost converter is analyzed in this paper. Soft switching conditions and peak voltage stress are studied as a function of the first switching cycle timing. In order to minimize the voltage overshoot and switching loss of the converter, the first switching period of each power pulse is chosen based on this analysis. A 1000 W, 3 MHz quasi-resonant boost converter with Class DE rectifier is implemented to verify the analysis and simulation results. A maximum efficiency of 96.7 percent is obtained. Further, the efficiency dropped less than three percent at one tenth of the load. About a 2% improvement in the light load efficiency is observed as compared to the conventional PDM method.
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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 | 0.000 |
| 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".