A Soft-Switching Bridgeless AC–DC Power Factor Correction Converter
Bibliographic record
Abstract
A new soft-switching, bridgeless power factor correction (PFC) boost converter is proposed for power supply and battery charging applications. The converter operates in both pulse width modulation (PWM) mode and resonant mode each switching cycle, and utilizes standard average current mode control. The converter is bridgeless, therefore eliminating the need for a front-end diode bridge rectifier. It operates in continuous conduction mode and achieves zero voltage switching (ZVS) for all switches. The proposed converter also reduces the turn-off losses of the PWM switches, therefore nearly eliminating switching losses. The output diodes operate with controlled di/dt turn-off, which reduces reverse-recovery losses. The PWM switches of the proposed converter can be driven with the same PWM signal, enabling simplified control. The detailed operation of the proposed converter is presented, including the conditions for ZVS operation and a stress analysis for the circuit components. Experimental results are presented for a 650-W prototype at 150-kHz switching frequency, universal ac input, and 400-V dc output. The proposed converter shows about 1% better efficiency and lower device temperatures at full load and 100-V ac input (maximum loss operating point) compared with the conventional hard switched PFC boost 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) | 0.003 | 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".