Parameter identification of Jiles‐Atherton model using SFLA

• Ramezan-Ali Naghizadeh ^[1] ; Behrooz Vahidi ^[1] ; Seyed Hossein Hosseinian ^[1]
  1. [1] Amirkabir University of Technology

     Amirkabir University of Technology

     Irán

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 31, Nº 4 (Special Issue: Selected papers from EHE 2011), 2012, págs. 1293-1309
• Idioma: inglés
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• Resumen

  • Purpose – The purpose of this paper is to implement a simple, fast and accurate heuristic method for parameter determination of Jiles‐Atherton (JA) hysteresis model for representing magnetization in electrical steel sheets. The performance of the method is validated using measured data and comparison with previous methods.

    Design/methodology/approach – JA model requires five parameters to represent the hysteretic behavior of ferromagnetic materials. In order to determine these parameters, measured hysteresis loop is used here to calculate a fitness function which is defined by comparing the measured and simulated magnetization loops. This fitness function is minimized by optimization algorithms.

    Findings – In total, four different measured hysteresis loops are studied in this paper. Each optimization algorithm is executed 50 times to investigate the convergence, speed, and accuracy of six methods. All methods begin with the same randomly generated initial parameters. Physical boundaries are used for parameters to avoid unaccepted results. Thorough examination of results shows that the proposed method is more appropriate than previously implemented methods for the parameter determination of Jiles‐Atherton model in all studied cases. The required parameters for each optimization method are also presented.

    Originality/value – Shuffled frog leaping algorithm (SFLA) is implemented for the first time for JA model parameter determination. The results show that SFLA is faster and more accurate in comparison with other methods. Furthermore, this algorithm is easy to implement and tune.