Model order reduction of electrical machines with multiple inputs

Mehrnaz Farzam Far, Anouar Belahcen, Paavo Rasilo, Stephane Clenet, Antoine Pierquin

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

In this paper, proper orthogonal decomposition (POD) method is employed to build a reduced-order model from a high-order nonlinear permanent magnet synchronous machine model with multiple inputs. Three parameters are selected as the multiple inputs of the machine. These parameters are terminal current, angle of the terminal current, and rotation angle. To produce the lower-rank system, snapshots or instantaneous system states are projected onto a set of orthonormal basis functions with small dimension. The reduced model is then validated by comparing the vector potential, flux density distribution, and torque results of the original model, which indicates the capability of using the POD method in the multivariable input problems. The developed methodology can be used for fast simulations of the machine.

Original languageEnglish
Article number7878567
Pages (from-to)3355-3360
Number of pages6
JournalIEEE Transactions on Industry Applications
Volume53
Issue number4
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Fingerprint

Decomposition
Permanent magnets
Torque
Fluxes

Keywords

  • Electrical machines
  • finite-element methods (FEM)
  • interior permanent magnet machine
  • model order reduction (MOR)
  • proper orthogonal decomposition (POD)

Cite this

Far, Mehrnaz Farzam ; Belahcen, Anouar ; Rasilo, Paavo ; Clenet, Stephane ; Pierquin, Antoine. / Model order reduction of electrical machines with multiple inputs. In: IEEE Transactions on Industry Applications. 2017 ; Vol. 53, No. 4. pp. 3355-3360.
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Model order reduction of electrical machines with multiple inputs. / Far, Mehrnaz Farzam; Belahcen, Anouar; Rasilo, Paavo; Clenet, Stephane; Pierquin, Antoine.

In: IEEE Transactions on Industry Applications, Vol. 53, No. 4, 7878567, 2017, p. 3355-3360.

Research output: Contribution to journalArticleScientificpeer-review

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