Modeling of anisotropic magnetostriction under alternating magnetization based on neural network-FFT model

• Yanli Zhang ^[1] ; Hang Zhou ^[1] ; Dianhai Zhang ^[1] ; Ziyan Ren ^[1] ; Dexin Xie ^[1]
  1. [1] Shenyang University of Technology

     Shenyang University of Technology

     China

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 36, Nº 6 (Special Issue: ICEF 2016), 2017, págs. 1706-1714
• Idioma: inglés
• Enlaces
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• Resumen

  • Purpose This paper aims to investigate the magnetostrictive phenomenon in a single electrical steel sheet, which may cause vibration and noise in the cores of transformers and induction motors. A measurement system of magnetostriction is created and the principal strain of magnetostriction is modeled. Furthermore, the magnetostriction property along arbitrary alternating magnetization directions is modeled.

    Design/methodology/approach A measurement system with a triaxial strain gauge is developed to obtain the magnetostrictive waveform, and the principal strain is computed in terms of the in-plane strain formula. A three-layer feed-forward neural network model is proposed to model the measured magnetostriction property of the electrical steel sheet.

    Findings The principal strain of magnetostriction of the non-oriented electrical steel has strong anisotropy. The proposed estimation model can be effectively used to model the anisotropic magnetostriction with an acceptable prediction time.

    Originality/value This paper develops the neural network combined with fast Fourier transform (FFT) to model the principal strain property of magnetostriction under alternating magnetizations, and its validation has been verified.