Three-phase dual active bridge converter design considerations
Bibliographic record
Abstract
In three-phase dual-active-bridge (DAB) converter, the zero-voltage-switching (ZVS) range, DC capacitor ripple current and converter efficiency are significantly impacted by the selectable system parameters, including transformer turn ratio n, switching frequency f and leakage inductance Lk. The converter performance also varies with different operation conditions, i.e., input voltage, output voltage and output power level. Therefore, these parameters need to be carefully selected to achieve optimized performances. In this paper, a comprehensive study of the parameter selection for the three-phase DAB converter is conducted under different operation conditions. A new parameter fL is defined to reduce the analysis from 3 dimensions to 2 dimensions, which simplifies the analysis of transformer turn ratio significantly. Furthermore, the effective operating area (EOA) of f and Lkare defined for feasible transformer designs. The current stress of switches and DC capacitors are studied in the EOA to help determine the range of f and Lk. Finally, the converter total loss and efficiency are analyzed at full and half load with different input and output voltages. The converter parameter (n, f, Lk) can be selected using ZVS and efficiency criteria. The tradeoff between high efficiency, wide ZVS area and low capacitor ripple current can be achieved accordingly.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".