application of wavelet transform to identify faulty igbts in 3-phase induction motor drives

B.Sindhuja,Yarlagadda Gouthami

Published in International Journal of Advanced Research in Electrical and Electronics Engineering

ISSN: 2321-4775          Impact Factor:1.6         Volume:3         Issue:1         Year: 26 June,2014         Pages:79-88

International Journal of Advanced Research in Electrical and Electronics Engineering

Abstract

Switching Devices (IGBT) used in Pulse Width Modulation (PWM) Voltage Source Inverter (VSI) feeding an induction motor often suffers from different types of faults due to ageing or performance in unfriendly environments. These faults which are caused due to improper contact points, problematic solder joints, poor connections etc are often less severe at their initial stages. Different variations of the above mentioned faulty cases in a PWM VSI driven induction motor are simulated in PSIM software and results are diagnosed to estimate condition of the inverter. Three phase line currents feeding the motor are recorded and transformed to d-q reference frame using Park’s Transformation for further analysis. From d and q current components thus obtained, the PVMs and PVACs are computed. Applying Continuous wavelet Transform on PVACs certain features are extracted which are helpful to distinguish healthy condition from faulty situations and segregates different cases of individual faulty IGBTs.

Kewords

PWM-VSI Drive; Park’s Tranformation; Park’s Vector Modulus(PVM); IGBT; Continuous Wavelet Transform; Scatter Plot

Reference

[1] K. Rothenhagen and F. W. Fuchs, “Performance of diagnosis methods for IGBT open circuit faults in voltage source active rectifier”, in Proc. 35th IEEE Annual Power Electronics Specialists Conference, vol. 6, pp. 4348-4354, 2004 [2] Kastha D. and Bose B.K., “Investigation of fault modes of voltage-fed inverter system for induction motor drive.”, IEEE Trans. Ind. Appl., vol 30, pp. 259-266, 1994 [3] P.J. Chrzan, and R. Szczesny, “Fault diagnosis of voltage-fed inverter for induction motor drive”, in Proc. IEEE International Symposium on Industrial Electronics, ISIE '96., vol. 2, pp. 1011-1016, 1996. [4] V. Fernão Pires, T. G. Amaral, D. Sousa and G.D.Marques, ” Fault detection of Voltage-Source Inverter using pattern recognition of the 3D current trajectory”, in Proc. IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON), pp. 617-621, July 2010. [5] J. Klima,” Analytical investigation of an induction motor drive under inverter fault mode operations” IEE Proc.-Electr. Power Appl., Vol. 150, No. 3, 255-262, May 2003. [6] Jang-Hwan Park, Dong-Hwa Kim, Sung-Suk Kim, Dae-Jong Lee and Myung-Geun Chun, “C-ANFIS based fault diagnosis for voltage-fed PWM motor drive systems” In Proc. IEEE Annual Meeting of Fuzzy Information Processing, NAFIPS '04, vol. 1, pp. 379-383, 2004. [7] Demba Diallo, Mohamed El Hachemi Benbouzid, Denis Hamad, and Xavier Pierre, “Fault detection and diagnosis in an induction machine drive: A pattern recognition approach based on concordia stator mean current vector”, IEEE Trans. on Energy Conversion, vol. 20, no. 3, pp. 512-519, 2005. [8] Abdesh M., Khan S.K., Azizur Rahman M., “A new wavelet based diagnosis and protection of faults in induction motor drives”, in Proc. IEEE Power Electronics Specialists Conference, 2008. pp. 1536-1541, 2008. [9] Aktas M., Turkmenoglu V., “Wavelet-based switching faults detection in direct torque control induction motor drives”, In Proc. IET Science,Measurement & Technology, vol. 4, no. 6, pp. 303-310, 2010. Hamid Nejjari and Mohamed El Hachemi Benbouzid, “Monitoring and diagnosis of induction motors electrical faults using a current Park’s Vector pattern learning approach”, IEEE Trans. on Industry Applications, vol. 36, no. 3, pp. 730–735, 2000. S. Das, C. Koley, P. Purkait, and S. Chakravorti, “Wavelet aided SVM Classifier for stator inter-turn fault monitoring in induction motors”, in Proc. IEEE PES General Meeting, USA, pp. 1-6, 2010. C. Koley, P. Purkait and S. Chakravorti, “Wavelet-Aided SVM Tool for impulse fault identification in transformers”, IEEE Trans. Power Delivery, vol. 21, no.3, pp. 1283 -1290, 2006. Robi Polikar, Wavelet Tutorial Part-I, II and III, [Online], Available http://users.rowan.edu/~polikar/WAVELETS/WTpart1.html