Parameter identification of Preisach model based on velocity-controlled particle swarm optimization method

The Preisach model is widely used to simulate the magnetic performance of transformers and motors in industrial applications. However, its parameter identification problem is still a complicated task. This paper proposed a new parameter identification method based on the velocity-controlled particle swarm optimization (VCPSO) algorithm combined with the closed-form Everett function. Firstly, the Preisach model is built through the closed-form Everett function, which gives an explicit form Preisach model. Secondly, the Preisach model’s parameter identification is realized by using the VCPSO algorithm, which only needs the limiting static hysteresis loop of the material. During the optimization process, the particle velocity is automatically adjusted to avoid falling into the optimal local solution. Finally, the obtained results are compared with the experimental hysteresis loop of the B30P150 silicon steel.

[1]  Jianguo Zhu,et al.  A Temperature-Dependent Hysteresis Model for Soft Ferrites , 2017, IEEE Transactions on Magnetics.

[2]  Dolores Gómez Pedreira,et al.  Mathematical analysis and numerical solution of models with dynamic Preisach hysteresis , 2019, J. Comput. Appl. Math..

[3]  Yasser Fouad,et al.  Identification of Preisach hysteresis model parameters using genetic algorithms , 2017, Journal of King Saud University - Science.

[4]  Sajid Hussain,et al.  An Efficient Implementation of the Classical Preisach Model , 2018, IEEE Transactions on Magnetics.

[5]  F. Preisach.  Über die magnetische Nachwirkung , 1935 .

[6]  E. Lomonova,et al.  Comparison of Preisach and Congruency-Based Static Hysteresis Models Applied to Non-Oriented Steels , 2020, IEEE Transactions on Magnetics.

[7]  W. M. Rucker,et al.  Identification procedures of Preisach model , 2002 .

[8]  Jianguo Zhu,et al.  Hysteresis Modeling of High-Temperature Superconductor Using Simplified Preisach Model , 2015, IEEE Transactions on Magnetics.

[9]  Zsolt Szabó,et al.  Implementation and identification of Preisach type hysteresis models with Everett Function in closed form , 2015 .

[10]  E Dlala,et al.  Efficient Algorithms for the Inclusion of the Preisach Hysteresis Model in Nonlinear Finite-Element Methods , 2011, IEEE Transactions on Magnetics.

[11]  Petru Andrei,et al.  Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method , 2007 .

[12]  Habib Dhahri,et al.  Implementation and Identification of Preisach Parameters: Comparison Between Genetic Algorithm, Particle Swarm Optimization, and Levenberg–Marquardt Algorithm , 2019, Arabian Journal for Science and Engineering.

[13]  Petru Andrei,et al.  First order reversal curves identification procedures for vector models of hysteresis , 2008 .

[14]  Zhigang Zhao,et al.  Measurement and modeling of hysteresis characteristics in ferromagnetic materials under DC magnetizations , 2019, AIP Advances.