On the Detection of Open and Short Fault in Switched Reluctance Motor With Classification

Abstract

Switched reluctance motors are tremendously increasing in usage. Monitoring the motor performance online is essential, in order to maintain the health of the motor as well as for a continual application process carried out by the SRM. Though certain fault detection methods are available based on comparator circuits, those methods are inefficient, because they need human attention to identify the faults. In this study, we propose a wavelet based SRM power drive fault detection and classification using BPN method. The currents flowing in different phases and the known states of all power switches helps us to find out the magnitude of the converter supply current, since an asymmetric bridge converter is used. In this study, short and open circuit fault occurrences in the converter power switches are presented for various coils of the stator windings. The proposed technique is able to detect these faults and identify the affected stator phase as well as the faulty arm along with the the time instant of fault occurrence.