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

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 voltage 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. 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. 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.