A Novel Approach for Sensitive Inter-turn Fault Detection in Induction Motor Under Various Operating Conditions

Abstract

Induction motors are subjected to thermal, electrical and mechanical stresses during continuous operation. If any of these stresses become excess than normal condition, it is a symptom of commencement of fault in the induction motor. If these faults are not detected at an incipient stage, it may result in the failure of the motor. Turn-to-turn short-circuit faults are the major causes for the stator winding insulation failure. In the proposed work, a novel approach is suggested to detect these faults in the induction motor. In this approach, the discrete wavelet transform (DWT) is performed on the Park’s vector modulus of current signals. The accuracy of DWT can be enhanced by selecting the proper wavelet type and its order along with levels of decomposition. With this context, an attempt is made to investigate the best-suited mother wavelet by testing various orthogonal wavelet functions on simulated signal and justified that the db44 is to be the best suitable wavelet for the detection of an inter-turn fault in the induction motor. The current signals are obtained experimentally under operating conditions with balanced supply voltages, unbalanced supply voltages, load slip variation and gradual variation in load condition and analyzed. The obtained results show the sensitivity and robustness of the proposed approach. The fault severity factor is also suggested for evaluating the severity of fault in the induction motor.