Fuzzy inference system for the characterization of SRM drives under normal and fault conditions

被引：16

作者：

Bouji, M ^{[1
]}

Arkadan, AA

Ericsen, T

机构：

[1] Marquette Univ, Dept EECE, Milwaukee, WI 53201 USA

[2] Off Naval Res, Arlington, VA 22217 USA

来源：

IEEE TRANSACTIONS ON MAGNETICS | 2001年 / 37卷 / 05期

关键词：

fault analysis; fuzzy inference systems; SRM drives;

D O I：

10.1109/20.952704

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 fuzzy inference system Is used to characterize switched reluctance motor, SRM, drive systems under normal and fault operating conditions. The Fuzzy Logic (FL),is applied for its ability to be very suitable for problems with large uncertainty. Knowledge about the system Is accumulated using coupled Finite-Element (FE) magnetic field and state space (SS) models. The validity of the FL based model is demonstrated by applying it to a prototype SRM drive system.

引用

页码：3745 / 3748

页数：4

共 8 条

[1] SWITCHED RELUCTANCE MOTOR DRIVE SYSTEMS DYNAMIC PERFORMANCE PREDICTION UNDER INTERNAL AND EXTERNAL FAULT CONDITIONS
ARKADAN, AA
KIELGAS, BW
[J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 1994, 9 (01) : 45 - 52
[2] Arkadan AA, 2000, IEEE T MAGN, V36, P1945, DOI 10.1109/20.877828
[3] Neurogenetic characterization of fault tolerant switched reluctance motor drives
Arkadan, ARA
Belfore, LA
[J]. IEEE TRANSACTIONS ON MAGNETICS, 1998, 34 (05) : 3612 - 3615
[4] Modeling faulted switched reluctance motors using evolutionary neural networks
Belfore, LA
Arkadan, ARA
[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1997, 44 (02) : 226 - 233
[5] BOUJI M, 2001, THESIS MARQUETTE U M
[6] Jyh-Shing Roger Jang, 1993, IEEE T SYSTEMS, V23
[7] LANGARI R, 1992, HDB INTELLIGENT CONT, P93
[8] Takagi T., 1983, P IFAC S FUZZ INF KN, P55, DOI [10.1016/S1474-6670(17)62005-6, DOI 10.1016/S1474-6670(17)62005-6]

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