Title :
Wavelet aided SVM classifier for stator inter-turn fault monitoring in induction motors
Author :
Das, S. ; Koley, C. ; Purkait, P. ; Chakravorti, S.
Author_Institution :
Dept. of Electr. Eng., HIT, Haldia, India
Abstract :
Early detection of faults in stator winding is crucial for reliable and economical operation of induction motors in industries. Whereas major winding faults can be easily identified from supply current magnitude, minor faults involving less than 5% of turns are not readily discernible. The present work documents experimental results for monitoring of minor short circuit faults in stator windings of induction motor. Motor line current has been analyzed using modern signal processing and data reduction tools combining Park´s transformation and Continuous Wavelet Transform (CWT). Support Vector Machine (SVM) based data classification tool has been used for fault characterization based on fault features extracted using CWT.
Keywords :
fault location; induction motors; power engineering computing; support vector machines; wavelet transforms; windings; CWT; Park transformation; continuous wavelet transform; data reduction tools; fault feature extraction; induction motors; motor line current; short circuit faults; signal processing; stator interturn fault monitoring; stator winding fault detection; supply current magnitude; support vector machine; wavelet aided SVM classifier; Concordia pattern; Induction motors; Park´s transformation; continuous wavelet transform; stator turn fault; support vector machine;
Conference_Titel :
Power and Energy Society General Meeting, 2010 IEEE
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-6549-1
Electronic_ISBN :
1944-9925
DOI :
10.1109/PES.2010.5589595
