Transfer Learning-Based Design Method for Cogging Torque Reduction in PMSM With Step-Skew Considering 3-D Leakage Flux

Yun Jae Won, Jae Hyun Kim, Soo Hwan Park, Ji Hyeon Lee, Soo Min An, Doo Young Kim, Myung Seop Lim

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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 article 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.

Original languageEnglish
Article number8204905
JournalIEEE Transactions on Magnetics
Volume59
Issue number11
DOIs
StatePublished - 1 Nov 2023

Keywords

  • 3-D leakage flux
  • cogging torque
  • deep neural network (DNN)
  • permanent magnet synchronous motors (PMSMs)
  • step-skew
  • transfer learning

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