High-Step-Up and High-Efficiency Fuel-Cell Power-Generation System With Active-Clamp Flyback–Forward Converter
Bibliographic record
Abstract
A high-efficiency fuel-cell power-generation system with an active-clamp flyback-forward converter is presented in this paper to boost a 12-V dc voltage into a 220-V 50-Hz ac voltage. The proposed system includes a high-efficiency high-step-up interleaved soft-switching flyback-forward converter and a full-bridge inverter. The front-end active-clamp flyback-forward converter has the advantages of zero-voltage-switching performance for all the primary switches, reverse-recovery-problem alleviation for the secondary output diodes, large voltage-conversion ratio, and small input-current ripple. Furthermore, there are two coupled inductors in the proposed converter. Each coupled inductor can work in the flyback mode when the corresponding main switch is in the turn-on state and in the forward mode when it is in the turnoff state, which takes full use of the magnetic core and improves the power density. In addition, the full-bridge inverter with anLClow-pass filter is adopted to provide low-total-harmonic-distortion ac voltage to the load. Therefore, high-efficiency and high-power-density conversion can be achieved in a wide input-voltage range by employing the proposed system. Finally, a 500-W prototype and another 1-kW converter are implemented and tested to verify the effectiveness of the proposed system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".