Title

Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model

Journal title

Archives of Electrical Engineering

Yearbook

2012

Volume

vol. 61

Issue

No 2 June

Authors

Knypiński, Łukasz ; Nowak, Lech ; Sujka, Piotr ; Radziuk, Kazimierz

Keywords

optimization ; hysteresis ; Jiles-Atherton model ; particle swarm optumization method

Divisions of PAS

Nauki Techniczne

Coverage

139-148

Publisher

Polish Academy of Sciences

Date

2012

Type

Artykuły / Articles

Identifier

DOI: 10.2478/v10171-012-0013-3 ; ISSN: 1427-4221 ; eISSN: 2300-2506

Source

Archives of Electrical Engineering; 2012; vol. 61; No 2 June; 139-148

References

S. Ali Pourmousavi (2010), Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization, IEEE Transactions on Sustainable Energy, 1, 3, 193, doi.org/10.1109/TSTE.2010.2061881 ; Benabou A. (2003), Comparasion of Preisach and Jiles-Atherton models to take into account hysteresis phenomenon for finite element analysis, Journal of Magnetism and Magnetic Materials, 261, 139, doi.org/10.1016/S0304-8853(02)01463-4 ; Boukhtache S. (2009), Optimized model for magnetic hysteresis in silicon-iron sheets by using the simulated annealing algorithm, International Journal of Applied Electromagnetics and Mechanics, 30, 1-2, 1. ; Dabrowski M. (2009), Efectiveness comparasion of non-evolutionary non-deterministic optimization methods in design electrical machines, Computer Applications in Electrical Engineering, 12. ; Engelbrecht A. (2007), Computational Intelligence, doi.org/10.1002/9780470512517 ; Hamel A. (2009), Particle swarm optimization for reconstruction of two-dimensional groove profiles in non destructive evaluation, null, 219. ; Jiles D. (1983), Ferromagnetic hysteresis, IEEE Transactions on Magnetisc, 19, 5, 2183, doi.org/10.1109/TMAG.1983.1062594 ; Jiles D. (1992), Numerical determination of hysteresis parameters for the modelling of magnetic properties using theory of ferromagnetic hysteresis, IEEE Transactions on Magnetics, 28, 1, 27, doi.org/10.1109/20.119813 ; Kennedy J. (1995), Particle Swarm Optimization, null, 1942. ; Kiranyaz S. (2010), Fractional particle swarm optimization in multidimensional search space, IEEE Transactions on Systems, Man and Cybernetics, 40, 2, 298, doi.org/10.1109/TSMCB.2009.2015054 ; Knypiński Ł. (2009), Application of non-deterministic algorithms in the electromagnetic devices optimal design, Computer Applications in Electrical Engineering, 216. ; Ivanyi A. (1997), Hysteresis models in electromagnetic computation. ; Marion R. (2008), Krahenbűhl, Identyfication of Jiles-Atherton model parameters using particle swarm optimization, IEEE Transactions on Magnetics, 44, 6, 894, doi.org/10.1109/TMAG.2007.914867 ; Meng K. (2010), A self-adaptive RBF neural network classifier for transformer fault analysis, IEEE Transactions on Power Systems, 25, 3, 1350, doi.org/10.1109/TPWRS.2010.2040491 ; Moossouni F. (2008), Comparison of two multi-agent algorithms: ACO and PSO for the optimization of brushless DC wheel motor, 3. ; Rashtchi V. (2011), A novel PSO-based technique for optimal design of protective current transformer, The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 30, 2, 505, doi.org/10.1108/03321641111101041 ; L. Santos Coelho (2010), A multiobjective Gaussian particle swarm approach applied to electromagnetic optimization, IEEE Transactions on Magnetisc, 46, 8, 3289, doi.org/10.1109/TMAG.2010.2047250 ; Sevkli Z. (2006), Practicle Swarm Optimization for the orienteering problem, null, 134. ; Szczygłowski J. (2001), Influence of eddy currents on magnetic hysteresis loops in soft magnetic material, Journal of Magnetism and Magnetic Material, 223, 97, doi.org/10.1016/S0304-8853(00)00584-9 ; Sujka P. (2008), Field-circuit algorithm of determining power losses in core taking magnetic hysteresis into account, Prace Naukowe Instytutu Maszyn, Napędów I Pomiarów Elektrycznych Politechniki Wrocławskiej, Studia i Materiały, 62, 28, 343. ; Trapanese M. (2011), Identification of parameters of the Jiles-Atherton model by neural networks, Journal of Applied Physics, 109, 7, doi.org/10.1063/1.3569735 ; Tang L. (2010), An improved particle swarm optimization algorithm for the hybrid flowshop scheduling to minimize total weighted completion time in process industry, IEEE Transactions on Control Systems Technology, 18, 6, 1303. ; Vasconcelos J. (2001), Improvements in Genetic Algorihms, IEEE Transactions on Magnetics, 37, 5, 1314, doi.org/10.1109/20.952626 ; Wilson D. (2001), Optimizing the Jiles-Atherton model of hysteresis by a genetic algorithm, IEEE Transactions on Magnetisc, 37, 2, 989, doi.org/10.1109/20.917182 ; <a target="_blank" href='http://www.seen.com.pl'>http://www.seen.com.pl</a>