[1] Zhuang Yingchao, Yu Haitao, Xia Jun, Hu Minqiang, et al. Optimization of linear induction machinesbased on a novel adaptive genetic algorithm [J]. Journal of Southeast University (English Edition), 2009, 25 (2): 203-207. [doi:10.3969/j.issn.1003-7985.2009.02.013]

Copy

Optimization of linear induction machinesbased on a novel adaptive genetic algorithm()

基于新型自适应遗传算法的直线感应电机的优化设计

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 2

Page:

203-207

Research Field:

Electrical Engineering

Publishing date:

2009-06-30

Info

Title:

Optimization of linear induction machinesbased on a novel adaptive genetic algorithm

基于新型自适应遗传算法的直线感应电机的优化设计

Author(s):

Zhuang Yingchao¹, Yu Haitao¹, Xia Jun², Hu Minqiang¹

¹School of Electrical Engineering, Southeast University, Nanjing 210096, China
² Jiangsu Science and Technology Museum, Nanjing 210013, China

庄英超¹, 余海涛¹, 夏军², 胡敏强¹

¹东南大学电气工程学院, 南京 210096; ²江苏科学宫, 南京 210013

Keywords:

adaptive genetic algorithm;  linear induction machine;  uniform design

自适应遗传算法;  直线感应电机;  均匀设计

PACS:

TM359.4

DOI:

10.3969/j.issn.1003-7985.2009.02.013

Abstract:

In order to improve the thrust-power ratio index of the linear induction motor(LIM), a novel adaptive genetic algorithm(NAGA)is proposed for the design optimization of the LIM.A good-point set theory that helps to produce a uniform initial population is used to enhance the optimization efficiency of the genetic algorithm.The crossover and mutation probabilities are improved by using the function of sigmoid and they can be adjusted nonlinearly between average fitness and maximal fitness with individual fitness.Based on the analyses of different structures between the LIM and the rotary induction motor(RIM)and referring to the analysis method of the RIM, the steady-state characteristics of the LIM that considers the end effects of the LIM is calculated and the optimal design model of the thrust-power ratio index is also presented.Through the comparison between the optimal scheme and the old scheme, the thrust-power ratio index of the LIM is obviously increased and the validity of the NAGA is proved.

为了提高直线感应电机的力能指标, 提出一种新型自适应遗传算法, 并对直线感应电机进行了优化设计.采用佳点集理论对遗传算法的初始化种群进行均匀设计, 提高了遗传算法的优化效率.同时利用sigmoid函数改进了交叉概率和变异概率, 使交叉率和变异率按照个体的适应度在平均适应度和最大适应度之间随sigmoid曲线进行非线性调整.在分析直线感应电机与旋转电机物理结构差异的基础上, 得到考虑边端效应的直线感应电机的稳态性能, 并给出直线感应电机力能指标的优化模型.通过对优化后的设计方案与原设计方案的比较发现:直线感应电机的力能指标显著提高, 验证了方法的有效性.

References:

[1] Wang Liqiang, Ji Qi. Optimum design of linear induction motor based on genetic algorithms [J].Small and Special Electrical Machines, 2008, 36(3):14-16.(in Chinese)
[2] Srinivas M, Patnaik L M.Adaptive probabilities of crossover and mutation in genetic algorithm [J].IEEE Trans on Systems, Man and Cybernetics, 1994, 24(4):656-667.
[3] Ren Ziwu, San Ye.Improved adaptive genetic algorithm and its application research in parameter identification [J].Journal of System Simulation, 2006, 18(1):41-44.(in Chinese)
[4] Ye Yunyue.The theory and application of linear machines[M].Beijing:China Machine Press, 2000:51-89.(in Chinese)
[5] Guo Huihao, Fan Yu, Shao Li. Application of adaptive genetic algorithm for parameter optimization of linear induction motor [J].Explosion-Proof Electric Machine, 2005, 40(3):16-18.(in Chinese)
[6] Wang Ruiming, Shi Haijun, Yang Qiong.Optimum design of power transformer based on an improved adaptive genetic algorithm [J].Jiangsu Electrical Engineering, 2005, 24(4):46-48.(in Chinese)
[7] Jin Jing, Su Yong.An improved adaptive genetic algorithm [J].Computer Engineering and Applications, 2005, 41(18):64-69.(in Chinese)
[8] Li Zhijun, Cheng Jiaxing.Uniform design of initial population of genetic algorithm based on good point set [J].Computer and Information Technology, 2007, 15(4):29-32.(in Chinese)
[9] Hassanpour Isfahani A, Ebrahimi B M, Lesani H.Design optimization of a low-speed single-sided linear induction motor for improved efficiency and power factor [J].IEEE Transactions on Magnetics, 2008, 44(2):46-51.

Memo

Memo:

Biographies: Zhuang Yingchao(1983—), male, gradate;Yu Haitao(corresponding author), male, doctor, professor, htyu@seu.edu.cn.
Citation: Zhuang Yingchao, Yu Haitao, Xia Jun, et al.Optimization of linear induction machines based on a novel adaptive genetic algorithm[J].Journal of Southeast University(English Edition), 2009, 25(2):203-207.

Common functions