Stator fault estimation in induction motors using particle swarm optimization

The use of induction motors is extensive in industry. The working conditions of these motors make them subject to many faults. These faults must be detected in an early stage before they lead to catastrophic failures. This paper presents a scheme for detecting inter-turn faults in the stator windings of induction motors and estimating the fault severity. Detection of incipient inter-turn faults prevents further insulation failure. The proposed algorithm monitors the spectral content of stator currents to detect the fault. After the fault is detected and identified, a particle swarm approach is used to estimate the fault severity. The swarm estimator update is based on the error between the measured data and a complete model of the faulty motor. An experimental setup is used to validate the developed scheme and to implement an online fault detector.

[1]  H. G. Sedding,et al.  Current monitoring for detecting inter-turn short circuits in induction motors , 2001 .

[2]  G.C. Soukup,et al.  Cause and analysis of stator and rotor failures in 3-phase squirrel cage induction motors , 1991, Conference Record of 1991 Annual Pulp and Paper Industry Technical Conference.

[3]  M. Dalva,et al.  A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries , 1994, Proceedings of IEEE Petroleum and Chemical Industry Technical Conference (PCIC '94).

[4]  Thomas G. Habetler,et al.  Transient model for induction machines with stator winding turn faults , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[5]  Gojko Joksimovic,et al.  The detection of inter-turn short circuits in the stator windings of operating motors , 2000, IEEE Trans. Ind. Electron..

[6]  F. van den Bergh,et al.  Training product unit networks using cooperative particle swarm optimisers , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[7]  Gabriela Ciuprina,et al.  Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag , 2002 .

[8]  T.A. Lipo,et al.  Multiple coupled circuit modeling of induction machines , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[9]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[10]  Paul C. Krause,et al.  Analysis of electric machinery , 1987 .

[11]  T. G. Habetler,et al.  Insulation failure prediction in AC machines using line-neutral voltages , 1998 .

[12]  Fong C. Teng.  Real-time control using Matlab Simulink , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[13]  Jim Penman,et al.  Induction machine condition monitoring with higher order spectra , 2000, IEEE Trans. Ind. Electron..

[14]  Thomas G. Habetler,et al.  An integrated, on-line, motor protection system , 1994 .

[15]  F. Filippetti,et al.  AI techniques in induction machines diagnosis including the speed ripple effect , 1996 .

[16]  J. Penman,et al.  Detection and location of interturn short circuits in the stator windings of operating motors , 1994 .

[17]  Mohamed El Hachemi Benbouzid.  A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..