Modeling of a Switched Reluctance Motor Under Stator Winding Fault Condition

被引：11
|
作者：
Chen, H. [1 ]
Han, G. [1 ]
Yan, W. [1 ]
Lu, S. [2 ]
Chen, Z. [3 ]
机构：
[1] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China
[2] Nanjing Inst Elect, Nanjing 210007, Jiangsu, Peoples R China
[3] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark
来源：
IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY | 2016年 / 26卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Artificial neural network; flux linkage; motor fault; switched reluctance motor; DIAGNOSIS;
D O I：
10.1109/TASC.2016.2539678
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：
A new method for modeling stator winding fault with one shorted coil in a switched reluctance motor (SRM) is presented in this paper. The method is based on artificial neural network (ANN), incorporated with a simple analytical model in electromagnetic analysis to estimate the flux-linkage characteristics of SRM under the stator winding fault. The magnetic equivalent circuit method with ANN is applied to calculate the nonlinear flux-linkage characteristics under stator winding fault condition. A stator winding fault 12/8 SRM prototype system is developed to verify the effectiveness of the proposed method. The results for a stator winding fault with one shorted coil are obtained from the proposed method and from the experimental work on a developed prototype. It is shown that the simulation results are close to the test results.
引用
收藏
页数：6
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