DOI QR코드

DOI QR Code

누설 인덕턴스를 포함한 DAB 컨버터용 고주파 변압기의 머신러닝 활용한 최적 설계

Machine-Learning Based Optimal Design of A Large-leakage High-frequency Transformer for DAB Converters

  • Eunchong, Noh (Dept. of Electrical & Computer Engineering, University of Seoul) ;
  • Kildong, Kim (Korea Railroad Research Institute (KRRI)) ;
  • Seung-Hwan, Lee (Dept. of Electrical & Computer Engineering, University of Seoul)
  • 투고 : 2022.08.05
  • 심사 : 2022.09.19
  • 발행 : 2022.12.20

초록

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.

키워드

과제정보

본 연구는 한국철도기술연구원 주요사업(PK2203F1)의 연구비 지원으로 수행되었습니다.

참고문헌

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