Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Wing Technique: A Novel Approach for the Detection of Stator Winding Inter-Turn Short Circuit and Open Circuit Faults in Three Phase Induction Motors

  • Received : 2010.11.27
  • Accepted : 2011.10.29
  • Published : 2012.01.20
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Abstract

This paper presents a novel approach based on the loci of instantaneous symmetrical components called "Wing Shape" which requires the measurement of three input stator currents and voltages to diagnose interturn insulation faults in three phase induction motors operating under different loading conditions. In this methodology, the effect of unbalanced supply conditions, constructional imbalances and measurement errors are also investigated. The sizes of the wings determine the loading on the motor and the travel of the wings while their areas determine the degree of severity of the faults. This approach is also applied to detect open circuit faults or single phasing conditions in induction motors. In order to validate this method, experimental results are presented for a 5 hp squirrel cage induction motor. The proposed technique helps improve the reliability, efficiency, and safety of the motor system and industrial plant. It also allows maintenance to be performed in a more efficient manner, since the course of action can be determined based on the type and severity of the fault.

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References

  1. P. Vas, Parameter estimation, condition monitoring and diagnosis of electrical machines, Oxford Science Publication, Clarendon Press, Oxford, 1993.
  2. A. J. M. Cardoso, S. M. A. Cruz, and D. S. B. Fonseca, "Inter-turn stator winding fault diagnosis in three phase induction motors, by Park's vector approach," IEEE Trans. Energy Convers., Vol. 14, No 3, pp. 595-598, Sep.1999. https://doi.org/10.1109/60.790920
  3. J. L. Kohler, J. Sottile, and F. C. Trutt, "Condition monitoring of stator windings in induction motors: Part I - Experimental investigation of the effective negative-sequence impedance detector," IEEE Tran. Ind. Appl., Vol. 38, No. 5, pp. 1447-1453, Sep./Oct. 2002. https://doi.org/10.1109/TIA.2002.802935
  4. J. Sottile, F. C. Trutt, and J. L. Kohler, "Condition monitoring of stator windings in induction motors: Part II-Experimental investigation of voltage mismatch detectors," IEEE Trans.Ind. Appl., Vol. 38, No. 5, pp. 1454-1459, Sep./Oct. 2002. https://doi.org/10.1109/TIA.2002.802921
  5. S.-B. Lee, R. M. Tallam, and T. G. Habetlor, "A robust, on-line, turnfault detection technique for induction machines based on monitoring the sequence components impedance matrix," IEEE Trans. Power Electron., Vol. 18, No. 3, pp. 865-872, May 2003. https://doi.org/10.1109/TPEL.2003.810848
  6. J.-H. Jung, J.-J. Lee, and B.-H. Kwon, "Online diagnosis of induction motors using mcsa," IEEE Trans. Ind. Electron., Vol. 53, No. 6, pp. 1842-1852, Dec. 2006. https://doi.org/10.1109/TIE.2006.885131
  7. H. Su and K. T. Chong, "Induction machine condition monitoring using neural network modeling," IEEE Trans. Ind. Electron., Vol. 54, No. 1 pp. 241-249, Feb. 2007. https://doi.org/10.1109/TIE.2006.888786
  8. M. S. Ballal, Z. J. Khan, H. M. Suryawanshi and R. L. Sonolikar, "Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear fault in induction motor," IEEE Trans. Ind. Electron., Vol. 54, No. 1, pp. 250-259, Feb. 2007. https://doi.org/10.1109/TIE.2006.888789
  9. J. F. Martins, V. Fern˜ao Pires, and A. J. Pires, "Unsupervised neuralnetwork- based algorithm for an on-line diagnosis of three-phase induction motor stator fault," IEEE Trans. Ind. Electron., Vol. 54, No. 1, pp. 259-264, Feb. 2007. https://doi.org/10.1109/TIE.2006.888790
  10. P. J. Tavner, "Review of condition monitoring of rotating electrical machines," IET Electr. Power Appl., Vol. 2, No. 4, pp. 215-247, 2008. https://doi.org/10.1049/iet-epa:20070280
  11. J. Cusido, L. Romeral, J. A. Ortega, J. A. Rosero, and A. G. 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. https://doi.org/10.1109/TIE.2007.911960
  12. G. K. Singh and S. A. S. Al Kazzaz, "Development of an intelligent diagnostic system for induction machine health monitoring," IEEE System Journal, Vol. 2, No. 2, pp. 273-288, Jun. 2008. https://doi.org/10.1109/JSYST.2008.924129
  13. 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. https://doi.org/10.1109/TIE.2008.2007527
  14. S. Grubic, J. M. Aller, B. Lu, and T. G. 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. https://doi.org/10.1109/TIE.2008.2004665
  15. J. Yun, K. Lee, K.-W. Lee, S. B. Lee, and J.-Y. Yoo, "Detection and classification of stator turn faults and high-resistance electrical connections for induction machines," IEEE Trans. Ind. Appl., Vol. 45, No. 2, pp. 666-675, Mar./Apr. 2009. https://doi.org/10.1109/TIA.2009.2013557

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  2. Detection of Winding Faults in Wound Rotor Induction Motor Using Loci of Direct and Quadrature Axes of Rotor Currents 2017, https://doi.org/10.1080/15325008.2017.1334102