Research Article Open Access

FAULT DETECTION IN SWITCHED RELUCTANCE MOTOR DRIVES USING DISCRETE WAVELET TRANSFORM AND K-MEANS CLUSTERING

V. S. Chandrika1 and A. Ebenezer Jeyakumar2
  • 1 Department of EEE, P.S.V. College of Engineering and Technology, Krishnagiri Dt., Tamilnadu, India
  • 2 Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India

Abstract

This study presents a novel method of detection of inter turn shorts based on k means clustering technique. In addition to inter turn short detection, the other faults like open, short, phase to phase faults and DC volt-age faults are detected through wavelet transforms and k means clustering. Open and short faults are classified using artificial neural network. All other faults are classified using Support Vector Machines (SVM). Switched reluctance motors are very popular in these days, because of ease in manufacturing and operation. Though an electronic circuit can detect the faults like open and short, the classification cannot be done effectively with electronic circuitry. More over an intelligent method can easily identify the fault and classify and hence the root cause of the fault may be guessed and rectified using this method of classification. This is highly possible with the time localization property of the wavelet transforms. So instant of fault occurrence can be detected along with the type of fault. The information used to include this intelligence in the system are just current waveforms, flux waveforms and torque waveforms. Inter turn shorts are very critical for a long run operation of the motor. Moreover, the early detection minimizes the faulty operation time and ensures the plant stability and saves the life of motor too. Hence an integrated system to detect the major faults under a simulation model has been proposed in this study.

American Journal of Applied Sciences
Volume 11 No. 3, 2014, 362-370

DOI: https://doi.org/10.3844/ajassp.2014.362.370

Submitted On: 15 November 2013 Published On: 30 December 2013

How to Cite: Chandrika, V. S. & Jeyakumar, A. E. (2014). FAULT DETECTION IN SWITCHED RELUCTANCE MOTOR DRIVES USING DISCRETE WAVELET TRANSFORM AND K-MEANS CLUSTERING. American Journal of Applied Sciences, 11(3), 362-370. https://doi.org/10.3844/ajassp.2014.362.370

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Keywords

  • Discrete Wavelet Transforms (DWT)
  • Inter Turn Shorts
  • K-Means Clustering
  • Switched Reluctance Motor (SRM)
  • Support Vector Machines (SVM)