A bridgeless hybrid-resonant PWM zero voltage switching boost AC-DC power factor corrected converter
Bibliographic record
Abstract
This paper presents a new bridgeless zero-voltage switching (ZVS) ac-dc boost power factor corrected converter for application in power supplies and battery chargers. This converter utilizes standard average-current-mode control while operating in both pulse-width-modulation (PWM) mode and resonant mode each switching cycle. This modulation technique can be referred to as hybrid-resonant PWM (HRPWM). The PWM switches of the proposed converter can be driven with the same PWM signal, so extra circuitry is not required to sense the positive or negative line-cycle operation. The proposed converter realizes ZVS for the main and auxiliary switches which nearly eliminates all switching losses and enables improved efficiency. The resonant operation provides controlled di/dt turn-off of the output diodes which reduces the reverse recovery losses and electromagnetic interference (EMI). Detailed operation of the proposed converter is provided, along with a stress analysis of the circuit components, and a design methodology to select the resonant components. Finally experimental results are shown for a prototype unit converting a universal ac input to a 650W, 400 V dc output, operating at 70 kHz switching frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".