| Title |
Machine-Learning Based Optimal Design of A Large-leakage High-frequency Transformer for DAB Converters |
| Authors |
Eunchong Noh ; Kildong Kim ; Seung-Hwan Lee |
| DOI |
https://doi.org/10.6113/TKPE.2022.27.6.507 |
| Keywords |
Multi-objective optimization; High-frequency transformer; Machine learning; DAB (Dual-Active-Bridge) converter |
| Abstract |
This study proposes an optimal design process for a high-frequency transformer that has a large leakage
inductance for dual-active-bridge converters. Notably, conventional design processes have large errors in
designing leakage transformers because mathematically modeling the leakage inductance of such transformers is
difficult. In this work, the geometric parameters of a shell-type transformer are identified, and finite element
analysis(FEA) simulation is performed to determine the magnetization inductance, leakage inductance, and
copper loss of various shapes of shell-type transformers. Regression models for magnetization and leakage
inductances and copper loss are established using the simulation results and the machine learning technique. In
addition, to improve the regression models’ performance, the regression models are tuned by adding featured
parameters that consider the physical characteristics of the transformer. With the regression models, optimal
high-frequency transformer designs and the Pareto front (in terms of volume and loss) are determined using
NSGA-II. In the Pareto front, a desirable optimal design is selected and verified by FEA simulation and
experimentation. The simulated and measured leakage inductances of the selected design match well, and this
result shows the validity of the proposed design process. |