Novel Wavelet ANN Technique to Classify Interturn Fault in Three Phase Induction Motor

摘要

Early detection of faults in stator winding of induction motor is crucial for reliable and economical operation of induction motor in industries. Whereas major winding faults can be easily identified from supply currents, minor faults involving less than 5 % of turns are not readily discernible. The present contribution reports experimental results for monitoring of minor short circuit faults in stator winding of induction motor. Motor line current has been analyzed using modern signal processing and data reduction tool combing Parka??s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Parka??s Current Vector Pattern. An online algorithm is tested successfully on three phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method.