Cascaded Half-Bridge-Based Bidirectional Multilevel Bridgeless PFC With Multioutput Ports
Bibliographic record
Abstract
Cascaded multilevel power factor correction (PFC) converters show great potential for applications that require high ac voltage input but low dc-bus voltage output, such as telecom power supply, battery formation, and Internet data centers. They are compact and put less voltage stress on power switches. To provide multidc outports with fewer power switches, a cascaded half-bridge-based multilevel multiport bridgeless PFC is proposed in this article. Compared with cascaded full-bridge multilevel PFC, the number of switches per power cell for the proposed PFC is reduced by half while maintaining the same dc outports. Due to the multilevel voltage, the volt–second on the boost inductor decreases, reducing the current ripple of the proposed PFC by$2n$times with the same boost inductance as the conventional totem-pole PFC. By splitting the power into$2n$cells with low voltage, the total switching losses are also reduced by$2n$times with the same equivalent switching frequency. In addition, the proposed rectifier also reduces conduction losses as lower voltage switches with smaller conduction loss are adopted in half-bridge cells compared with conventional totem-pole PFC rectifiers. Finally, these benefits are analyzed and validated with experiments.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".