Name
Title | ||
|---|---|---|
Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIE.2014.2303785 | Industrial Electronics, IEEE Transactions |
Keywords | Field | DocType |
brushless machines,finite element analysis,genetic algorithms,neural nets,permanent magnet machines,random processes,regression analysis,rotors,stators,support vector machines,synchronous machines,FE calculations,air-gap torque,artificial neural networks,automatable process,brushless PMSMs,brushless permanent-magnet synchronous machines,current vector,data-based system identification techniques,direct components,dynamic motor models,finite element simulations,full electrical period,genetic programming,linear regression,nonlinear behavior,nonlinear effects,quadrature components,random forests,rotor angle,stator flux,support vector machines,symbolic regression,Artifical neural networks (ANNs),Brushless machine,artifical neural network,brushless machine,cogging torque,field-oriented control,genetic programming,genetic programming (GP),modeling,permanent magnet,random forests,random forests (RFRs),symbolic regression,torque ripple | Nonlinear system,Control theory,Support vector machine,Direct torque control,Control engineering,Genetic programming,Test data,Engineering,Artificial neural network,System identification,Symbolic regression | Journal |
Volume | Issue | ISSN |
61 | 11 | 0278-0046 |
Citations | PageRank | References |
6 | 0.69 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerd Bramerdorfer | 1 | 55 | 5.92 |
| Winkler, S.M. | 2 | 6 | 0.69 |
| Kommenda, M. | 3 | 6 | 1.03 |
| Weidenholzer, G. | 4 | 6 | 0.69 |