Multipurpose Design of Optimized Current Controller for Bridgeless Totem-Pole Power Factor Correction Converters
Bibliographic record
Abstract
This research aims to design a control strategy for the conventional silicon-carbide (SiC)-based totem-pole bridgeless power factor correction (TPBPFC) converter which covers power quality requirements and the improved performance of the converter. An optimized adaptive hysteretic band of the current controller to increase the efficiency and meet reliability and safe operation of the inductor far from the saturation area is developed. The improved efficiency is realized using a hybrid approach: negative conduction mode (NCM), continuous conduction mode (CCM), and a band-adaptable hysteresis controller. Soft-switching conditions for all power switches in NCM and lower conduction losses in CCM are optimally achieved. Minimized total losses of the converter, the inductor’s safe operation far from the saturation area, and the improved reliability of the converter are obtained simultaneously. A 1.2 kW SiC-based TPBPFC prototype is finally tested for vivificating the proposed strategies. The efficiency of the converter at 20% rated power is more than 98.4%, and the maximum efficiency obtained is 99.1%. The power factor is above 0.99 at maximum load, and the input current’s total harmonic distortion (THD) is below 3%. In addition, the suggested approach has exceptional thermal properties under typical heat dissipation settings, as demonstrated by the thermal test.
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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) | 0.001 | 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) | 0.002 | 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".