Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means

Abstract

Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.