Switchable power converters for multiple input voltage level power factor correction applications
Bibliographic record
Abstract
In this paper, two switchable power converters, integrated SEPIC/boost and flyback/boost, are described, analyzed and designed for power factor correction applications with multiple line input voltages (e.g. 120 Vrms and 277 Vrms). The range-selection switch can configure the converter to boost topology at low line input voltages, and to SEPIC or flyback topology at high line input voltage. As a result, the output DC voltage can be set to an intermediate level for overall power conversion efficiency improvement and system cost reduction. boundary conduction mode (BOM) is considered for both topological configurations for the following benefits, 1) Near resistive input characteristics; 2) Smaller inductor sizes compared with continuous conduction mode (CCM); 3) Lower switch current stress compared with discontinuous conduction mode (DCM); and 4) Simple controller. Analytic and experimental results showed that the component current stresses and losses in the switchable converters are significantly lower than those in conventional SEPIC or flyback converters. It is also shown that it is possible to share the same power stage components and the same control IC in the switchable converters with guaranteed absolute stability
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".