A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

Abstract A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data.

[1]  D. Jiles,et al.  Theory of ferromagnetic hysteresis (invited) , 1984 .

[2]  Baodong Bai,et al.  Identification of the Jiles-Atherton model parameters using simulated annealing method , 2011, 2011 International Conference on Electrical Machines and Systems.

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

[4]  F.R. Fulginei,et al.  Softcomputing for the identification of the Jiles-Atherton model parameters , 2005, IEEE Transactions on Magnetics.

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

[6]  Lech Nowak,et al.  Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model , 2011 .

[7]  Mouloud Feliachi,et al.  Minor hysteresis loops model based on exponential parameters scaling of the modified Jiles–Atherton model , 2012 .

[8]  Qingxin Yang,et al.  Optimization of hysteresis parameters for the Jiles-Atherton model using a genetic algorithm , 2004 .

[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]  Alessandro Salvini,et al.  Genetic algorithms and neural networks generalizing the Jiles-Atherton model of static hysteresis for dynamic loops , 2002 .

[11]  Nelson Sadowski,et al.  The inverse Jiles-Atherton model parameters identification , 2003 .

[12]  Baodong Bai,et al.  Evaluationof Jiles-Atherton Hystersis model’s parameters using mix of simplex method and simulated annealing method , 2008, 2008 International Conference on Electrical Machines and Systems.

[13]  Krzysztof Chwastek,et al.  Identification of a hysteresis model parameters with genetic algorithms , 2006, Math. Comput. Simul..

[14]  Andrew D. Brown,et al.  Optimizing the Jiles-Atherton model of hysteresis by a genetic algorithm , 2001 .

[15]  Wenmei Huang,et al.  Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators , 2007, Neurocomputing.

[16]  Hajime Igarashi,et al.  On the parameter identification and application of the Jiles-Atherton hysteresis model for numerical modelling of measured characteristics , 1999 .

[17]  D. Jiles,et al.  Numerical determination of hysteresis parameters for the modeling of magnetic properties using the theory of ferromagnetic hysteresis , 1992 .

[18]  Wei Li,et al.  Hysteresis Modeling for Electrical Steel Sheets Using Improved Vector Jiles-Atherton Hysteresis Model , 2011, IEEE Transactions on Magnetics.

[19]  Z. Włodarski.  Extraction of hysteresis loops from main magnetization curves , 2007 .

[20]  Krzysztof Chwastek,et al.  An alternative method to estimate the parameters of Jiles–Atherton model , 2007 .

[21]  F. A. Guerra,et al.  Multiobjective Exponential Particle Swarm Optimization Approach Applied to Hysteresis Parameters Estimation , 2012, IEEE Transactions on Magnetics.

[22]  Lin Li,et al.  A modified method for jiles-atherton hysteresis model and its application in numerical simulation of devices involving magnetic materials , 2010, Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation.

[23]  D. Jiles,et al.  Theory of ferromagnetic hysteresis , 1986 .

[24]  N. Sadowski,et al.  Real coded genetic algorithm for Jiles-Atherton model parameters identification , 2004, IEEE Transactions on Magnetics.

[25]  Mouloud Feliachi,et al.  Modified Jiles―Atherton model and parameters identification using false position method , 2010 .