A Direct Actual-Power Control Scheme for Current-Fed Dual-Active-Bridge DC/DC Converter Based on Virtual Impedance Estimation
Bibliographic record
Abstract
High dynamic performance is an essential requirement for the dual-active-bridge (DAB) dc/dc converters. As dc voltage sources, they should maintain the desired output voltage instantly under all working conditions. However, the previous literature mainly focus on the dynamic control of the voltage-fed DAB converters, and the existing control schemes for the current-fed DAB converters achieve limited dynamic performance. Aiming at improving the dynamic performance, a direct actual-power control (DAPC) scheme based on virtual impedance estimation (VIE) is proposed for the current-fed DAB converters in this article. The proposed DAPC scheme is based on a parallel structure instead of the series structure of existing control schemes, and it realizes fast dynamic control through combining actual power control with the VIE method. The proposed DAPC scheme can obtain the fastest transient response for the output voltage without voltage overshoot in transient conditions, such as load step change, input voltage fluctuation, and the desired output voltage step change. Besides, a leakage inductor precharging method is integrated into the DAPC scheme to avoid the current mismatching. Finally, the proposed DAPC schemes are compared with two existing control schemes and tested in a scale-down experimental prototype. Experimental results verify the effectiveness of the proposed DAPC scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".