Browse > Article
http://dx.doi.org/10.5370/JEET.2015.10.6.2326

A Fault Severity Index for Stator Winding Faults Detection in Vector Controlled PM Synchronous Motor  

Hadef, M. (Laboratoire L2EI. Universite de Jijel)
Djerdir, A. (Laboratoire IRTES-SET. Universite de Technologie de Belfort-Montbeliard)
Ikhlef, N. (Laboratoire L2EI. Universite de Jijel)
Mekideche, M.R. (Laboratoire L2EI. Universite de Jijel)
N'diaye, A. O. (Laboratoire IRTES-SET. Universite de Technologie de Belfort-Montbeliard)
Publication Information
Journal of Electrical Engineering and Technology / v.10, no.6, 2015 , pp. 2326-2333 More about this Journal
Abstract
Stator turn faults in permanent magnet synchronous motors (PMSMs) are more dangerous than those in induction motors (IMs) because of the presence of spinning rotor magnets that can be turned off at will. Condition monitoring and fault detection and diagnosis of the PMSM have been receiving a growing amount of attention among scientists and engineers in the past few years. The aim of this study is to propose a new detection technique of stator winding faults in a three-phase PMSM. This technique is based on the image analysis and recognition of the stator current Concordia patterns, and will allow the identification of turn faults in the stator winding as well as its correspondent fault index severity. A test bench of a vector controlled PMSM motor behaviors under short circuited turn in two phases stator windings has been built. Some experimental results of the phase to phase short circuits have been performed for diagnosis purpose.
Keywords
Permanent magnet synchronous motor(PMSM); Stator turn faults Park's vector approach; Image composition; Pattern recognition; Fault severity index (FSI);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 J. Arellano-Padilla, M. Sumner, and C. Gerada, “Winding condition monitoring scheme for a permanent magnet machine using high-frequency injection,” IET Electric power applications, vol. 5, Iss. 1, pp. 89-99, 2011.
2 Y. Nyanteh, C. Edrington, S. Srivastava, and D. Cartes, “Application of artificial intelligence to realtime fault detection in permanent-magnet synchronous machines”, IEEE Trans. On Industry Applications, vol. 49, no. 3, pp. 1205-1214, May/June, 2013.   DOI
3 A. Sarikhani, and O. A. Mohamed, “Inter-turn fault detection in PM synchronous machines by physics-based back electromotive force estimation,” IEEE Trans. On Industrial Electronics, vol. 60, no. 8, pp. 3472-3484, Aug., 2013.   DOI
4 J. A. Farooq, T. Rominosoa, A. Djerdir, and A. Miraoui, “Modelling and simulation of stator winding inter-turn faults in permanent magnet synchronous motors,” Compel Journal, vol. 27, no. 4, pp.887-896, 2008.   DOI
5 M. Hadef, M. R. Mekideche, and A. Djerdir, “Vector controlled permanent magnet synchronous motor (PMSM) drive with stator turn fault,” Proceedings of XIX International Conference, ICEM, Rome, 6-8 Sep. 2010.
6 T. Ishikawa, Y. Seki, and N. Kurita, “Analysis for fault detection of vector-controlled permanent magnet synchronous motor with magnet defect,” IEEE Trans. On Magnetics, vol. 49, no. 5, pp. 2331-2334, May, 2013.   DOI
7 Y. Da, X. Shi, and M. Krishnamurthy,“ A new approach to fault diagnosis for permanent magnet synchronous machines using electromagnetic signature analysis, IEEE Trans. On Power Electronics, vol. 28, no. 8, pp. 4104-4112, Aug., 2013.   DOI
8 B. L. R. Samaga, K. P. Vittal, “Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis,” Electrical power and energy systems, 35(2012), 180-185.   DOI
9 J. C. Urresty, J. R. Riba, L. Romeral, “A back-emf based method to detect magnet failures in PMSMs,” IEEE Trans. On Magnetics, vol. 49, no. 1, pp. 591-598, Jan., 2013.   DOI
10 B.M. Ebrahimi, J. Faiz, S. Lotfi-Fard, P. Pillay, “Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform,” Mechanical systems and signal processing 30(2012), 131-145.   DOI
11 F. Zidani, D. Diallo, M. E. H. Benbouzid, & R. Nait-Said, “A fuzzy-based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive,”, IEEE Trans. On Industrial Electronics, vol. 55, no. 2, pp. 586-593, Feb., 2008.   DOI
12 H. H. Lee, N. T. Nguyen, & J. M. Kwon, “Bearing fault diagnosis using fuzzy inference optimized by neural network and genetic algorithm,” Journal of electrical engineering & technology, vol. 2, no. 3, pp. 353-357, 2007.   DOI
13 J. Faiz, M. Ojaghi, “Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: A review,” Mechatronics 19(2009), 2-13.   DOI
14 A. Soualhi, G. Clerc, and H. Razik, “Detection and diagnosis of faults in induction motor using an improved artificial and clustering technique”, IEEE Trans. On Industrial Electronics, vol. 60, no. 9, pp. 4053-4062, Sep., 2013.   DOI
15 Y. Gritli, L. Zarri, C. Rossi, F. Filippetti, G.A. Capolino, and D. Casadei, “Advanced diagnosis of electrical faults in wound-rotor induction machines”, IEEE Trans. On Industrial Electronics, vol. 60, no. 9, pp. 4012-4024, Sep., 2013.   DOI
16 V. N. Ghate, & S. V. Dudul, “Induction machine fault detection using generalized feed forward neural network,” Journal of electrical engineering & technology, vol. 4, no. 3, pp. 389-395, 2009.   DOI
17 A. Medoued, A. Lebaroud, A. Laifa, & D. Sayad, “Classification of induction machine faults using time frequency representation and particle swarm optimization,” Journal of electrical engineering & technology, vol. 9, no. 1, pp. 170-177, 2014.   DOI
18 G. Didier, E. Ternisien, O. Caspary, and H. Razik, “Fault detection of broken rotor bars in induction motor using a global fault index,” IEEE Trans. On Industry Applications, vol.42, no.1, pp.79-88, January/February, 2006.   DOI
19 J. F. Martins, V. Fernao Pires, and A. J. Pires, “Unsupervised neural network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault,” IEEE Trans. On Industrial Electronics, vol. 54, no. 1, pp. 253-264, Feb., 2007.
20 J. F. Martins, V. F. Piers, and T. Amaral, “Induction motor fault detection and diagnosis using a current state pattern recognition,” Pattern Recognition Letters, vol. 32, pp. 321-328, 2011.   DOI
21 L. M. R. Olivier, A. J. M. Cardoso, “Extended Park’s vector approach-based differential protection of three-phase power tarnsformers,” IET Electr. Power Appl, vol. 6, Iss. 8, pp. 463-472, 2012.   DOI
22 S.M.A. Cruz, and A.J.M. Cardoso, “Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by extended park’s vector approach,” IEEE Trans. On Industry Applications, vol. 37, no. 5, pp. 1227-1233, Sep/Oct., 2001.   DOI