[Oral Presentation]Identification of Jiles-Atherton Model Parameters Using Improved Genetic Algorithm

Identification of Jiles-Atherton Model Parameters Using Improved Genetic Algorithm

ID：73 Submission ID：1732 View Protection：ATTENDEE Updated Time：2020-10-27 13:16:00 Hits：264 Oral Presentation

Start Time：2020-11-02 16:15 （Asia/Shanghai）

Duration：15min

Session：[E] Electrotechnical Theory and New Electromagnetic Technology » [E1] Session 5 and Session 10

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Abstract

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.

Keywords

Jiles-Atherton model, hysteresis loop, genetic algorithm, parameters, convergence speed

Speaker

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.