3D analysis of influence of stator winding asymmetry on axial flux

• Wojciech Pietrowski ^[1]
  1. [1] Poznań University of Technology

     Poznań University of Technology

     Poznań, Polonia

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 32, Nº 4 (Special Issue: Modelling of magnetic and electric circuits), 2013, págs. 1278-1286
• Idioma: inglés
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• Resumen

  • Purpose – The diagnostics of electrical machines is a very important task. The paper seeks to present a study and analysis of stator winding asymmetry in induction motors. The purpose of this paper is presentation of coupling two numerical techniques, a finite element analysis and an artificial neural network, in diagnostics of electrical machines.

    Design/methodology/approach – A finite element method (FEM) analysis and time‐stepping are applied for the study of IM with stator winding asymmetry. One of the asymmetry symptoms is an axial flux. In order to determine the level of winding asymmetry a generalized regression neural network has been considered. The result of FFT analysis of axial flux and electromagnetic torque was the input vector to artificial neural network. The output vector is the level of asymmetry. The algorithms are tested using a set data obtained from numerical simulation. The emphasis of this structure is on accurate approximation of the value of the stator winding asymmetry.

    Findings – The axial flux, as the symptom of stator winding asymmetry, can contribute to better detection of the asymmetry in stator winding.

    Originality/value – It is argued that the proposed method based on axial flux and electromagnetic torque is capable of performing detection of the asymmetry in stator winding. The generalized regression neural network can be used in health monitoring system as an inference module.