Multilevel Optimization for Surface Mounted PM Machine Incorporating With FEM

In this paper, multilevel genetic algorithm (MLGA) is presented to solve the optimization of surface mounted permanent magnet synchronous machine (SPMSM), which has features of mixed continuous and discrete design variables, multi-modal objective functions, etc. Firstly, the multilevel optimization...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2009-10, Vol.45 (10), p.4700-4703
Main Authors: Shuhong Wang, Shuhong Wang, Xiangjun Meng, Xiangjun Meng, Ningning Guo, Ningning Guo, Haibo Li, Haibo Li, Jie Qiu, Jie Qiu, Jian Guo Zhu, Jian Guo Zhu, Youguang Guo, Youguang Guo, Dikai Liu, Dikai Liu, Yi Wang, Yi Wang, Wei Xu, Wei Xu
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Constraint optimization
Design engineering
Design optimization
Dielectrics and electrical insulation
Electric machines
Finite element method
Finite element method (FEM)
Genetic algorithms
Laboratories
Magnetic analysis
Magnetic flux
Magnetism
Mathematical models
Multilevel
multilevel genetic algorithm
Optimization
Optimization methods
permanent magnet (PM) machine
Permanent magnets
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Summary:In this paper, multilevel genetic algorithm (MLGA) is presented to solve the optimization of surface mounted permanent magnet synchronous machine (SPMSM), which has features of mixed continuous and discrete design variables, multi-modal objective functions, etc. Firstly, the multilevel optimization problem is described by using the problem matrix. The values in the problem matrix are deduced by correlation analysis. Secondly, the architecture and implementation of MLGA are carried out. Thirdly, the new algorithm is applied to a bilevel optimization of SPMSM to verify this multilevel optimization. The results compared with those of traditional genetic algorithm (GA) and discussions of the multilevel optimization are presented.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2009.2024875