Double-Frequency Method Using Differential Evolution for Identifying Parameters in the Dynamic Jiles–Atherton Model of Mn–Zn Ferrites

The Jiles-Atherton (J-A) model is suitable for modeling ferrite-core-based inductors and transformers but relies on accurate identification of the relevant parameters from experimental data. In this paper, a differential evolution method is utilized to extract the seven parameters required for the dynamic J-A model. An appropriate set of objective functions for the differential evolution algorithm is proposed, which includes optimization around six critical points on the B-H curve. These critical points include the coercive force, the maximum magnetic field and flux densities, and the remanence force. By doing so, the stability and efficiency of the optimization process have been significantly increased and convergence is guaranteed. Both pseudonumerical modeling and parameter extraction based on experimental data are used to verify the proposed technique. A dual-frequency optimization technique is also proposed such that the influence of inaccuracy in the experimental data can be reduced. The accuracy of the extracted seven parameters in the dynamic J-A model is improved. Finally, the seven parameters are used in the dynamic J-A model to calculate the B-H curve at frequencies other than those used in the parameters' extraction. Very good agreement is obtained between the measured and modeled characteristics.

[1]  Domenico Grimaldi,et al.  Automatic and accurate evaluation of the parameters of a magnetic hysteresis model , 2000, IEEE Trans. Instrum. Meas..

[2]  Jacek Izydorczyk,et al.  Extraction of Jiles and Atherton parameters of ferrite from initial magnetization curves , 2006 .

[3]  J. Pleite,et al.  Accurate modeling of voltage and current waveforms with saturation and power losses in a ferrite core via two-dimensional finite elements and a circuit simulator , 2010 .

[4]  Bernardo Tellini,et al.  Measurement of Asymmetric Minor Loops in Soft Ferrites Up to Medium Frequencies , 2009, IEEE Transactions on Instrumentation and Measurement.

[5]  David Jiles.  Frequency Dependence Of Hysteresis Curves In Ferromagnetic Materials , 1993 .

[6]  M.-A. Raulet,et al.  Identification of Jiles–Atherton Model Parameters Using Particle Swarm Optimization , 2008, IEEE Transactions on Magnetics.

[7]  Bernardo Tellini,et al.  A new method to characterize magnetic hysteresis in soft ferrites up to high frequencies , 2004, IMTC 2004.

[8]  Luc Dupré,et al.  Impact reduction of the uncertain geometrical parameters on magnetic material identification of an EI electromagnetic inductor using an adaptive inverse algorithm , 2012 .

[9]  L. Michaeli,et al.  Automatic and accurate evaluation of the parameters of the magnetic hysteresis model , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[10]  M. Sumner,et al.  Characteristics of Jiles–Atherton Model Parameters and Their Application to Transformer Inrush Current Simulation , 2008, IEEE Transactions on Magnetics.

[11]  Alessandro Ferrero,et al.  A method for the determination of the parameters of the hysteresis model of magnetic materials , 1994 .

[12]  DaMing Zhang,et al.  Comprehensive Modeling of Resonant Inverter for Driving Fluorescent Lamp With the Consideration of Nonlinear Magnetization of Inductor , 2011, IEEE Transactions on Magnetics.

[13]  Leandro dos Santos Coelho,et al.  Solution of Jiles-Atherton vector hysteresis parameters estimation by modified Differential Evolution approaches , 2012, Expert Syst. Appl..

[14]  G. Stumberger,et al.  Parameter Identification of the Jiles–Atherton Hysteresis Model Using Differential Evolution , 2008, IEEE Transactions on Magnetics.

[15]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .