Transfer Learning-Based Design Method for Cogging Torque Reduction in PMSM with Step-Skew Considering 3-D Leakage Flux
- Authors
- Won, Yun-Jae; Kim, Jae-Hyun; Park, Soo-Hwan; Lee, Ji-Hyeon; An, Soo-Min; Kim, Doo-Young; Lim, Myung Seop
- Issue Date
- May-2023
- Publisher
- IEEE-INSTITUTE OF ELECTRICAL and ELECTRONICS ENGINEERS
- Keywords
- 3-D leakage flux; Cogging torque; deep neural network (DNN); Design methodology; Forging; Geometry; Harmonic analysis; permanent magnet synchronous motors (PMSMs); Rotors; step-skew; Torque; transfer learning; Transfer learning
- Citation
- IEEE International Conference on Magnetics (INTERMAG), pp.1 - 5
- Indexed
- SCOPUS
- Journal Title
- IEEE International Conference on Magnetics (INTERMAG)
- Start Page
- 1
- End Page
- 5
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190379
- DOI
- 10.1109/TMAG.2023.3294601
- ISSN
- 0018-9464
- Abstract
- Step-skew is a common technique for eliminating the cogging torque of a target harmonic order in permanent magnet synchronous motors (PMSMs). However, when step-skew is applied to the rotor, the cogging torque of the target harmonic order is not completely eliminated due to 3-D leakage flux. Therefore, the 3-D leakage flux should be considered in designing a PMSM with step-skew for cogging torque reduction. The most accurate way to consider the 3-D leakage flux is to perform 3-D finite element analysis (FEA), but it has the disadvantage of high computation time. To resolve this challenge, this paper proposes a design method that utilizes transfer learning to reduce the time for 3-D FEA while maintaining accuracy. Through the proposed method, a large amount of 2-D FEA-based data and a small amount of 3-D FEA-based data are used instead of a large amount of 3-D FEA-based data, with similar accuracy as using a large amount of 3-D FEA-based data, and the computational time is highly reduced. Finally, a prototype is fabricated and tested to verify the validity of the proposed design method for cogging torque reduction. IEEE
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