Model-based stator interturn short-circuit fault detection and diagnosis in induction motors

In this paper, a novel model-based method for induction motor with stator inter-turn short-circuit fault detection is presented. The proposed technique is based on the whiteness of innovation sequence developed by the standard extended Kalman filter. Nonlinear Generalized Likelihood Ratio method is applied to identify the faulty phase along with its severity. This technique just requires current sensors which are available in most induction motor drive systems to provide good controllability, and induction motor design details are not necessary. Computer simulations are carried out for a 4-hp squirrel cage induction motor using MATLAB environment. The results show the superiority of the proposed method as it provides better estimates for stator interturn fault detection.

[1]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[2]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[3]  Scott D. Sudhoff,et al.  Analysis of Electric Machinery and Drive Systems , 1995 .

[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]  Rastko Zivanovic,et al.  Modelling and simulation of stator and rotor fault conditions in induction machines for testing fault diagnostic techniques , 2009 .

[6]  T.A. Lipo,et al.  Complex vector model of the squirrel cage induction machine including instantaneous rotor bar currents , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[7]  Raphaël Romary,et al.  Offline and Online Methods for Stator Core Fault Detection in Large Generators , 2013, IEEE Transactions on Industrial Electronics.

[8]  V. Agarwal,et al.  Induction Machines: A Novel, Model based Non-invasive Fault Detection and Diagnosis Technique , 2008, 2008 Joint International Conference on Power System Technology and IEEE Power India Conference.

[9]  Thomas G. Habetler,et al.  A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems , 2008, IEEE Transactions on Industrial Electronics.

[10]  P. J. Unsworthc,et al.  Modelling and simulation of induction motors with inter-turn faults for diagnostics , 2005 .

[11]  Sachin C. Patwardhan,et al.  Intelligent state estimation for fault tolerant nonlinear predictive control , 2009 .

[12]  Murat Barut,et al.  Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters , 2007, IEEE Transactions on Industrial Electronics.

[13]  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.

[14]  Chee-Mun. Ong,et al.  Dynamic simulation of electric machinery : using MATLAB/SIMULINK , 1997 .

[15]  Leila Parsa,et al.  Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors , 2011, IEEE Transactions on Industrial Electronics.

[16]  Hamid A. Toliyat,et al.  Transient analysis of cage induction machines under stator, rotor bar and end ring faults , 1995 .

[17]  S. Narasimhan,et al.  A Supervisory Approach to Fault-Tolerant Control of Linear Multivariable Systems , 2002 .

[18]  S. Kumar,et al.  A critical evaluation and experimental verification of Extended Kalman Filter, Unscented Kalman Filter and Neural State Filter for state estimation of three phase induction motor , 2011, Appl. Soft Comput..