Least square support vector machine network-based modeling for switched reluctance starter/generator

Issue title: Selected Papers from the 14th International Symposium on Applied Electromagnetics and Mechanics (ISEM 2009), Part I

Article type: Research Article

Authors: Ding, Wen^{a; *} | Liang, Deliang^a

Affiliations: [a] School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: This paper presents the least square support vector machine (LS-SVM) network as a new tool to develop the model of the switched reluctance machine (SRM) and predict the dynamic performances of SRM system. The basic premise of LS-SVM regression is that it forms a very efficient mapping structure for the nonlinear SRM. By using the measured sample data of SRM, the LS-SVM is designed to learn the nonliear magnetization data with rotor position and phase current as input, and the corresponding flux linkage and torque as output. It has a good capability of generalization and is computationally efficient. With the developed modeling method, a LS-SVM current-dependent inverse flux linkage model and a LS-SVM torque model are used to simulate the dynamic performances of a 6/4 SRM operating as a starter/generator, and the accuracy of the model is tested via comparison to the measurements of steady state phase current characteristics.

Keywords: Flux linkage, least square support vector machine, modeling, switched reluctance machine, torque

DOI: 10.3233/JAE-2010-1139

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 33, no. 1-2, pp. 403-413, 2010