A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper....

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.36558-36567
Main Authors: Li, Ke, Cheng, Guoyu, Sun, Xiaodong, Yang, Zebin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Bearingless induction motor
Computer simulation
Couplings
finite element analysis
Finite element method
Flux
gray wolf optimization
Induction motors
least squares support vector machine
Mathematical models
Model accuracy
Modelling
nonlinear model
Optimization
Parameters
Permanent magnet motors
Regularization
Reluctance motors
Support vector machines
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Summary:The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to determine the optimal kernel parameter and regularization parameter of the LSSVM automatically. In this method, all BIMs flux linkage data are obtained from the finite-element method. In this paper, the relationship between input and output of the nonlinear flux linkage model is studied, and the precision model of GWO-LSSVM flux linkage is obtained. The simulation results demonstrate that the GWO-LSSVM model has high prediction accuracy and strong prediction ability. In addition, the GWO-LSSVM model is compared with other models. From this simulation comparison, it can be concluded that GWO-LSSVM modeling has the characteristics of higher accuracy.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2905247