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

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper an algorithm and computer code for the identification of the hysteresis parameters of the Jiles-Atherton model have been presented. For the identification the particle swarm optimization method (PSO) has been applied. In the optimization procedure five design variables has been assumed. The computer code has been elaborated using Delphi environment. Three types of material have been examined. The results of optimization have been compared to experimental ones. Selected results of the calculation for different material are presented and discussed.
Rocznik
Strony
139--148
Opis fizyczny
Bibliogr. 25 poz., tab., rys.
Twórcy
autor
autor
autor
Bibliografia
  • [1] Ali Pourmousavi S., Hashem Nehrir M., Colson C.M., Wang C., Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization. IEEE Transactions on Sustainable Energy 1(3): 193-201 (2010).
  • [2] Benabou A., Clenet S., Piriou F., Comparasion of Preisach and Jiles-Atherton models to take into account hysteresis phenomenon for finite element analysis. Journal of Magnetism and Magnetic Materials 261: 139-160 (2003).
  • [3] Boukhtache S., Azoui B., Féliachi M., 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): 1-7 (2009).
  • [4] Dabrowski M., Rudeński A., Efectiveness comparasion of non-evolutionary non-deterministic optimization methods in design electrical machines. Computer Applications in Electrical Engineering, pp. 12-23 (2009).
  • [5] Engelbrecht A.P., Computational Intelligence. John Wiley & Sons Ltd. (2007).
  • [6] Hamel A., Mohellebi H., Feliachi M., Hocini F., Particle swarm optimization for reconstruction of two-dimensional groove profiles in non destructive evaluation. Book of Digest of the XIV Inter national Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering ISEF’2009, Arras, 10-12 September, pp. 219-220 (2009).
  • [7] Jiles D., Atherton D.L., Ferromagnetic hysteresis. IEEE Transactions on Magnetisc 19(5): 2183-2185 (1983).
  • [8] Jiles D., Thoelke J.B., Devine M.K., Numerical determination of hysteresis parameters for the modelling of magnetic properties using theory of ferromagnetic hysteresis. IEEE Transactions on Magnetics 28(1): 27-35 (1992).
  • [9] Kennedy J., Eberhart R., Particle Swarm Optimization. Proceedings of the International Conference on Neural Networks, Perth, Australia, pp. 1942-1948 (1995).
  • [10] Kiranyaz S., Ince T., Yildirim A., Gabbouj M., Fractional particle swarm optimization in multidimensional search space. IEEE Transactions on Systems, Man and Cybernetics 40(2): 298-319 (2010).
  • [11] Knypiński Ł., Nowak L., Radziuk K., Kowalski K., Application of non-deterministic algorithms in the electromagnetic devices optimal design. Computer Applications in Electrical Engineering, pp. 216-232 (2009).
  • [12] Ivanyi A., Hysteresis models in electromagnetic computation. Akademiai Kiadó, Budapest (1997).
  • [13] Marion R., Scoretti R., Raulet M.A., Krahenbűhl, Identyfication of Jiles-Atherton model parameters using particle swarm optimization. IEEE Transactions on Magnetics 44(6): 894-897 (2008).
  • [14] Meng K., Yang Dong Z., Hui Wang D., Po Wang K., A self-adaptive RBF neural network classifier for transformer fault analysis. IEEE Transactions on Power Systems 25(3): 1350-1360 (2010).
  • [15] Moossouni F., Brisset S., Brochet P., Comparison of two multi-agent algorithms: ACO and PSO for the optimization of brushless DC wheel motor. Studies in Computational Intelligence, Intelligent Computer Techniques in Applied Electromagnetics, Springer, pp. 3-10 (2008).
  • [16] Rashtchi V., Bagheri A., Shabani A., Fazli S., 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), 505-518 (2011).
  • [17] Santos Coelho L., Hultmann Ayala H.V., Alotto P., A multiobjective Gaussian particle swarm approach applied to electromagnetic optimization. IEEE Transactions on Magnetisc 46(8): 3289-3292 (2010).
  • [18] Sevkli Z., Erdogan Sevilgen F., Keles Ö., Practicle Swarm Optimization for the orienteering problem. Proceedings of the International Symposium on Computer Information Sciences ISCIS 2006, Istanbul, 1-3 November, pp. 134-143 (2006).
  • [19] Szczygłowski J., Influence of eddy currents on magnetic hysteresis loops in soft magnetic material. Journal of Magnetism and Magnetic Material 223: 97-102 (2001).
  • [20] Sujka P., Field-circuit algorithm of determining power losses in core taking magnetic hysteresis into account (in polish), Prace Naukowe Instytutu Maszyn, Napędów I Pomiarów Elektrycznych Politechniki Wrocławskiej, Studia i Materiały 62(28): 343-348 (2008).
  • [21] Trapanese M., Identification of parameters of the Jiles-Atherton model by neural networks. Journal of Applied Physics 109(7): 07D355 (2011).
  • [22] Tang L., Wang X., 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): 1303-1314 (2010).
  • [23] Vasconcelos J.A., Ramirez J.A., Takahashi R.H.C., Saldanha R.R, Improvements in Genetic Algorihms. IEEE Transactions on Magnetics 37(5): 1314-3417 (2001).
  • [24] Wilson D.R., Ross J.N., Brown A.D., Optimizing the Jiles-Atherton model of hysteresis by a genetic algorithm. IEEE Transactions on Magnetisc 37(2): 989-993 (2001).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS4-0002-0089
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