Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means
DOI:
10.33395/sinkron.v8i2.12207Keywords:
Short circuit Detection; Induction Motor; Wavelet Transform; Haar Wavelet; Fuzzy C-meansAbstract
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.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
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