Research on the Torque Density Optimization of a Semi-Embedded Permanent Magnet Wind Turbine Based on the Non-Dominated Sorting Genetic Algorithm II and Magnetic Pole Offset

Wei Li, Dongrui Wang (), Zuoxia Xing and Changjie Sun
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Wei Li: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Dongrui Wang: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Zuoxia Xing: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Changjie Sun: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

Energies, 2024, vol. 17, issue 24, 1-13

Abstract: To improve the torque density (TD) of a permanent magnet wind turbine (PMWT), this paper proposes a magnetic pole offset semi-embedded structure based on the traditional semi-embedded structure. Firstly, the principle of how magnetic pole offset can increase the torque is explained. Then, based on the non-dominated sorting genetic algorithm II (NSGA-II), the ratio k of the inner and outer diameters of the stator is optimized to make the motor quality and efficiency reach the best state. On this basis, the TD is further optimized by utilizing the magnetic pole offset angle. The results show that when the magnetic pole offset angle is 0.5°, the TD reaches the maximum value of 13.95 Nm/kg, with an increase of 3.33%. Finally, the no-load performance and load performance of the two structures are compared to highlight the advantages of the magnetic pole offset structure.

Keywords: permanent magnet wind turbine (PMWT); torque density (TD); semi-embedded; magnetic pole offset; non-dominated sorting genetic algorithm II (NSGA-II) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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