Analytical modeling of Force and Electromagnetic Torque in Bearing-less Switched-Reluctance Machines Considering Rotor Inclinment

Document Type : Original Article

Authors

1 Professor, Shahrekord University, Shahrekord, Iran

2 PhD student, Shahrekord University, Shahrekord, Iran

3 Professor, GREEN Research School, University of Lorraine, France Groupe de Recherche en Energie Electrique de Nancy (GREEN)

Abstract

Abstract: In the presented paper, an accurate model is proposed for the magnetic levitation force components in switched reluctance machines. The machine geometry, stator winding configuration, instantaneous rotor angular position and the current waveforms are considered as the model input variables. The rotor surface magnetic stress is obtained as a function of time and space by using Maxwell’s stress tensor. The required flux density components are obtained by developing an analytical model based on sub-domain analysis. By post-processing of the obtained flux density components the effect of each coil current on producing magnetic levitation force is revealed. The developed models for the magnetic levitation force components are multi-variable second-order functions. Using these functions needs low computational burden for predicting the force components. In addition, by using a conformal transformation the model is extended to consider the rotor eccentricity and inclined rotor condition. The efficacy and the efficiency of the provided model is verified by means of finite element analysis. The maximum obtained error between the proposed analytical model and FEA for the presented simulation are 5.7%, 3.9% and 4.1% (0.52mN.m, 0.04N and 1.2N) for the electromagnetic torque and the x and y components of the levitation force, respectively. In addition, the required simulation time with the same processor for the proposed model and FEA are 420ms and 14.26s, respectively.

Keywords

Applied Electromagnetics
Volume 12, Issue 2 - Serial Number 29
Autumn and winter 2024
December 2024

Files

History

  • Receive Date: 04 August 2024
  • Revise Date: 29 September 2024
  • Accept Date: 15 October 2024
  • Publish Date: 22 October 2024

Share

How to cite

Statistics