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Identification of Jiles-Atherton Model Parameters Using Improved Genetic Algorithm
编号：73 访问权限：仅限参会人 更新：2020-10-27 13:16:00 浏览：257次 口头报告

报告开始：2020年11月02日 16:15（Asia/Shanghai）

报告时间：15min

所在会场：[E] Electrotechnical Theory and New Electromagnetic Technology [E1] Session 5 and Session 10

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摘要
Accurate acquisition of Jiles-Atherton(J-A) model parameters is the key to hysteresis modeling. This paper proposes an improved genetic algorithm to obtain the J-A model parameters accurately. The physical meaning of the J-A model is explained, and the influence of the J-A model parameters on the hysteresis loop of the iron core is studied. The fundamental of the traditional genetic algorithm is explained and on this basis, the methods for improvement are proposed. The improved genetic algorithm, un-improved genetic algorithm and the particle swarm optimization(PSO) are used to identify J-A model parameters. The result shows that compared with the un-improved genetic algorithm and the PSO, the convergence speed and fitting degree are improved in the proposed improved genetic algorithm and it is not easy to fall into local optimum.
 
关键词
Jiles-Atherton model, hysteresis loop, genetic algorithm, parameters, convergence speed
报告人
Zhou Junjie
Student Sichuan University

Junjie Zhou was born in China. He received the bachelor’s degree in the School of Electrical Engineering from Sichuan University, Chengdu, China, in 2020. He is currently working toward the master’s degree in the School of Electrical Engineering from Sichuan University, Chengdu, China.
 

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重要日期

  • 会议日期

    11月02日

    2020

    至

    11月04日

    2020

  • 10月27日 2020

    初稿截稿日期

  • 11月03日 2020

    报告提交截止日期

  • 11月04日 2020

    注册截止日期

  • 11月17日 2020

    终稿截稿日期

主办单位
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
承办单位
Huazhong University of Science and Technology
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