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

被引:4
作者
Ding, Wen [1 ]
Liang, Deliang [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Elect Engn, Xian 710049, Peoples R China
关键词
Flux linkage; least square support vector machine; modeling; switched reluctance machine; torque; SIMULATION; DRIVES;
D O I
10.3233/JAE-2010-1139
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:403 / 413
页数:11
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