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:1.8051

@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