FUZZY BASED FAULT DETECTION AND CONTROL FOR 6/4 SWITCHED RELUCTANCE MOTOR

Document Type: Research Paper

Authors

1 DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING, PONDICHERRY ENGINEERING COLLEGE, PONDICHERRY-605014, INDIA

2 DEPARTMENT OF INSTRUMENTATION ENGINEERING, MIT CAMPUS, ANNA UNIVERSITY, CHROMEPET, CHENNAI-600044, INDIA

Abstract

Prompt detection and diagnosis of faults in industrial systems are
essential to minimize the production losses, increase the safety of the operator
and the equipment. Several techniques are available in the literature to achieve
these objectives. This paper presents fuzzy based control and fault detection for a
6/4 switched reluctance motor. The fuzzy logic control performs like a classical
proportional plus integral control, giving the current reference variation based on
speed error and its change. Also, the fuzzy inference system is created and rule
base are evaluated relating the parameters to the type of the faults. These rules are
fired for specific changes in system parameters and the faults are diagnosed. The
feasibility of fuzzy based fault diagnosis and control scheme is demonstrated by
applying it to a simulated system.

Keywords


[1] A. A. Arkadan and B. W. Kielgas, Switched reluctance motor drive systems dynamic
performance prediction under internal and external fault conditions, IEEE Trans. on
Energy Conversion, 9 (1994), 45-52.
[2] I. Husain and M. N. Anwa, Fault analysis of switched reluctance motor drives, IEEE
Conference, (1999), 41-43.

[3] C. C. Lee, Fuzzy logic control systems: fuzzy logic controller–Part I, IEEE Transaction on
systems, man and Cybernetics, 20, 404-418.
[4] J. M. Mendel, Fuzzy logic systems for engineering: tutorial, Proceedings of IEEE, 83
(1995).
[5] T. J. E. Miller, Switched reluctance motors and their control, Hillsboro, OH: Magna
Physics, (1993).
[6] S. Mir, M. E. Elbuluk and I. Husain, Torque-ripple minimization in switched reluctance
motor, IEEE Transactions on Industry Applications, 35 (1999).
[7] S. Mir, I. Husain and M. E. Elbuluk, Switched reluctance motor modeling with on-line
parameter identification, IEEE Transactions on Industry Applications, 34 (1998).
[8] R. Muthu and E. El Kanzi, Fuzzy logic control of A pH neutralization process, IEEE -
ICECS-(2003), 1066-1069.
[9] A.V. Radun, Design Considerations for the switched reluctance motor, IEEE Trans. on
Industry Applications, 31 (1995), 1079-1087.
[10] M. G. Rodrigues, W. I. Suemitsu, P. Branco, J. A. Dente and L. G. B. Rolim, Fuzzy logic
control of a switched reluctance motor, Coppe/UFRJ-Federal University of Rio de
Janerio.
[11] K. Russa, I. Husain and M. E. Elbuluk, A Self-tuning controller for switched reluctance
motors, IEEE Transactions on Power Electronics, 15 (2000).
[12] F. Soares and P. J. Costa Branco, Simulation of a 6/4 switched reluctance motor based on
matlab/simulink environment, IEEE Transactions on Aero Space and Electronic Systems,
37 (2001).
[13] C. M. Stephens, Fault detectionand managementSystem for fault tolerant switched reluctance
motor, IEEE-Industry Applications Society Conf. Rec., (1989), 574-578.