RECENT ADVANCES IN MODELING AND ONLINE DETECTION OF STATOR INTERTURN FAULTS IN ELECTRICAL MOTORS

P. V. Sarode; P. S. Gadhe

doi:10.17605/OSF.IO/QWJKV

Authors

P. V. Sarode Assistant Professor, Department of Electrical Engineering, MSOET, Akola (M.S.), India
P. S. Gadhe Associate Professor, Department of Electrical Engineering, MSOET, Akola (M.S.), India

DOI:

https://doi.org/10.17605/OSF.IO/QWJKV

Keywords:

Analytical model, artificial intelligence (AI), condition monitoring, fault diagnosis, fault tolerance, feature extraction, induction machines, permanent-magnet (PM) machines, turn fault.

Abstract

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

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References

C. Gerada, K. Bradley, M. Sumner, P. Wheeler, S. Pickering, J. Clare, C. Whitley, and G. Towers, “The results do mesh,” IEEE Ind. Appl. Mag., vol. 13, no. 2, pp. 62–72, Mar./Apr. 2007.

A. Bonnett and C. Yung, “Increased efficiency versus increased reliability,” IEEE Ind. Appl. Mag., vol. 14, no. 1, pp. 29–36, Jan./Feb. 2008.

A. Bonnett and G. Soukup, “Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors,” IEEE Trans. Ind. Appl., vol. 28, no. 4, pp. 921–937, Jul./Aug. 1992. [4] R. Tallam, S. B. Lee, G. Stone, G. Kliman, J. Yoo, T. Habetler, and R. Harley, “A survey of methods for detection of stator-related faults in induction machines,” IEEE Trans. Ind. Appl., vol. 43, no. 4, pp. 920–933, Jul./Aug. 2007.

A. Bellini, F. Filippetti, C. Tassoni, and G.-A. Capolino, “Advances in diagnostic techniques for induction machines,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4109–4126, Dec. 2008.

S. Grubic, J. Aller, B. Lu, and T. Habetler, “A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4127–4136, Dec. 2008.

S. Kia, H. Henao, and G.-A. Capolino, “Digital signal processing for induction machines diagnosis—A review,” in Proc. 33rd IEEE IECON, Nov. 2007, pp. 1155–1162.

Q. Wu and S. Nandi, “Fast single-turn sensitive stator inter-turn fault detection of induction machines based on positive and negative equence third harmonic components of line currents,” in Conf. Rec. IEEE IAS Annu. Meeting, Oct. 5–9, 2008, pp. 1–8.

J. Cusido, L. Romeral, J. Ortega, J. Rosero, and A. Garcia Espinosa, “Fault detection in induction machines using power spectral density in wavelet decomposition,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 633–643, Feb. 2008.

W. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Ind. Appl. Mag., vol. 7, no. 4, pp. 26–34, Jul./Aug. 2001.

K. Kim, “Simple on-line fault detecting scheme for short-circuited turn in a PMSM through current harmonic monitoring,” IEEE Trans. Ind. Electron., 2010, to be published.

A. da Silva, R. Povinelli, and N. Demerdash, “Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes,” IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1310– 1318, Mar. 2008.

P. S. Barendse and P. Pillay, “A new algorithm for the detection of faults in permanent magnet machines,” in Proc. 32nd IEEE IECON, Nov. 6–10, 2006, pp. 823–828.

S. Cruz and A. Cardoso, “Multiple reference frames theory: A new method for the diagnosis of stator faults in three-phase induction motors,” IEEE Trans. Energy Convers., vol. 20, no. 3, pp. 611–619, Sep. 2005.