期刊
IEEE ACCESS
卷 9, 期 -, 页码 143188-143197出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3121271
关键词
Rotors; Torque; Magnetic flux; Windings; Toroidal magnetic fields; Magnetic cores; Shape; Axial flux; Vernier machine; torque density; cogging torque; genetic algorithm; 3D-FEA
资金
- Brain Pool (BP) Program - Ministry of Science and ICT through the National Research Foundation of Korea [2019H1D3A1A01102988]
- Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education [2016R1D1A1B01008058]
- Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) - Ministry of Trade, Industry and Energy, Korea Government [20204030200090]
This paper presents the performance improvement of a multi-rotor axial flux vernier permanent magnet machine with a proposed two-stage parallelogram-shaped PM, which reduces cogging torque and torque ripples due to its skew effect. The study also includes comparative analysis between conventional and proposed shape PM, as well as optimization using genetic algorithm and kriging method for further performance improvement.
Permanent magnet vernier machines (PMVMs) are becoming progressively attentive because of their high efficiency and high torque density and can thus be utilized for direct-drive applications such as wind power and electric vehicles etc. This paper presents performance improvement of multi-rotor axial flux vernier permanent magnet (MR-AFVPM) machine with a proposed two-stage parallelogram-shaped PM. The proposed shaped PM reduces the cogging torque and torque ripples due to its skew effect. Furthermore, it also presents a comparative analysis of the conventional and proposed shape PM. Then 3D finite element analysis (FEA) is used for comparative analysis. Genetic algorithm (GA) associated with kriging method based on LHS is introduced and is used to optimize the proposed shaped PM for further performance improvement in terms of cogging torque, back EMFs, torque ripples, VTHD, output torque, flux density distributions, power factor and output power of the analyzed machines which are validated by 3D-FEA.
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