Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Bramerdorfer:2014:ieeeIE,
  author =       "Gerd Bramerdorfer and Stephan M. Winkler and 
                 Michael Kommenda and Guenther Weidenholzer and 
                 Siegfried Silber and Gabriel Kronberger and Michael Affenzeller and 
                 Wolfgang Amrhein",
  title =        "Using FE Calculations and Data-Based System
                 Identification Techniques to Model the Nonlinear
                 Behavior of PMSMs",
  journal =      "IEEE Transactions on Industrial Electronics",
  year =         "2014",
  month =        nov,
  volume =       "61",
  number =       "11",
  pages =        "6454--6462",
  keywords =     "genetic algorithms, genetic programming, brushless
                 machine, permanent magnet, cogging torque, torque
                 ripple, modelling, field-oriented control, symbolic
                 regression, artificial neural network, random forests",
  DOI =          "doi:10.1109/TIE.2014.2303785",
  ISSN =         "0278-0046",
  size =         "9 pages",
  abstract =     "This article investigates the modelling 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
                 modelling 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
                 component and the air gap torque, both modelled as
                 functions of the rotor angle and the current vector.
                 The data is preprocessed according to theoretical
                 results on potential harmonics in the targets as
                 functions of the rotor angle. A variety of modelling
                 strategies were explored: linear regression, support
                 vector machines, symbolic regression using genetic
                 programming, random forests, and artificial neural
                 networks. The motor models were optimised for each
                 training technique and their accuracy was then compared
                 on the basis of 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
                 especially with additional test data.",
  notes =        "Also known as \cite{6729026}",
}

Genetic Programming entries for Gerd Bramerdorfer Stephan M Winkler Michael Kommenda Guenther Weidenholzer Siegfried Silber Gabriel Kronberger Michael Affenzeller Wolfgang Amrhein

Citations