Study on multiobjective optimization of SRM based on FOA

doi:10.13382/j.jemi.2017.07.023

Home > Archive>Volume 31, Issue 7, 2017 >1152-1158. DOI:10.13382/j.jemi.2017.07.023

Study on multiobjective optimization of SRM based on FOA
DOI:
                        10.13382/j.jemi.2017.07.023
                    
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                        Rao Shenghua1Rao Shenghua
NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China
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Zhang Xiaoping1Zhang Xiaoping
NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China
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Zhang Zhu2Zhang Zhu
College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
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Zhao Xuan1Zhao Xuan
NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China
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Affiliation:1. NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China; 2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Clc Number:TM352
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Abstract:

The traditional multi objective optimization algorithms are complex with too many regulation parameters and large amount of calculation, and they are easily trapped in the local optimal solution. Aiming at the problem above, a novel optimization method based on fruit fly optimization algorithm(FOA) is proposed in the paper. The switched reluctance motor (SRM) is then modeled by extreme learning machine algorithm, and optimized by FOA. Finally, the proposed algorithm is verified by various simulations, and the comparative analysis with traditional PSO are carried out. It is demonstrated that better optimization results of torque ripple and efficiency are achieved by the proposed algorithm, and it has fewer regulation parameters, faster convergence speed and avoidance of local optimal solution. Therefore, the proposed algorithm has better application value in the optimization of SRM.

Key words:SRM; fruit fly optimization algorithm(FOA); extreme learning machine

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Online: September 14,2017
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