High Frequency Phase Variable Model of Electric Machines from Electromagnetic Field Computation

Authors

  • O. A. Mohammed Department of Electrical and Computer Engineering Energy Systems Laboratory Florida International University, Miami, FL 33174, USA
  • S. Ganu Department of Electrical and Computer Engineering Energy Systems Laboratory Florida International University, Miami, FL 33174, USA
  • N. Abed Department of Electrical and Computer Engineering Energy Systems Laboratory Florida International University, Miami, FL 33174, USA
  • S. Liu Department of Electrical and Computer Engineering Energy Systems Laboratory Florida International University, Miami, FL 33174, USA
  • Z. Liu Department of Electrical and Computer Engineering Energy Systems Laboratory Florida International University, Miami, FL 33174, USA

Keywords:

High Frequency Phase Variable Model of Electric Machines from Electromagnetic Field Computation

Abstract

This paper presents a high frequency phase variable model of electric machines obtained using finite element (FE) analysis. The model consists of the low frequency phase variable model in parallel with a high frequency winding branch. The resistance and inductance of individual winding turns are calculated by magnetodynamic FE analysis while capacitance is calculated by electrostatic FE analysis. With the obtained parameters, a distributed model was formed based on the winding arrangement. The order of the distributed winding model is reduced using the Kron reduction technique to form the lumped parameter high frequency winding branch. A Permanent Magnet synchronous motor (PMSM) with its operating inverter is used as an example. The developed model is then used to evaluate the motor-inverter interaction. The results show the ability of the developed model to represent the motor’s high frequency behavior under different operating conditions.

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Published

2022-06-18

How to Cite

[1]
O. A. . Mohammed, S. . Ganu, N. . Abed, S. . Liu, and Z. . Liu, “High Frequency Phase Variable Model of Electric Machines from Electromagnetic Field Computation”, ACES Journal, vol. 22, no. 1, pp. 164–171, Jun. 2022.

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