Using FE calculations and data-based system identification techniques to model the nonlinear behavior of PMSMs

Gerd Bramerdorfer, Stephan M. Winkler, Michael Kommenda, Guenther Weidenholzer, Siegfried Silber, Gabriel Kronberger, Michael Affenzeller, Wolfgang Amrhein

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

This paper investigates the modeling of brushless permanent-magnet synchronous machines (PMSMs). The focus is on deriving an automatable process for obtaining dynamic motor models that take nonlinear effects, such as saturation, into account. The modeling is based on finite element (FE) simulations for different current vectors in the $dq$ plane over a full electrical period. The parameters obtained are the stator flux in terms of the direct and quadrature components and the air-gap torque, both modeled as functions of the rotor angle and the current vector. The data are preprocessed according to theoretical results on potential harmonics in the targets as functions of the rotor angle. A variety of modeling strategies were explored: linear regression, support vector machines, symbolic regression using genetic programming, random forests, and artificial neural networks. The motor models were optimized for each training technique, and their accuracy was then compared based on the initially available FE data and further FE simulations for additional current vectors. Artificial neural networks and symbolic regression using genetic programming achieved the highest accuracy, particularly with additional test data.

Original languageEnglish
Article number6729026
Pages (from-to)6454-6462
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume61
Issue number11
DOIs
Publication statusPublished - Nov 2014

Keywords

  • artifical neural network
  • Brushless machine
  • cogging torque
  • field-oriented control
  • genetic programming
  • modeling
  • permanent magnet
  • random forests
  • symbolic regression
  • torque ripple

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