FAULT DIAGNOSIS OF DFIG USING WAVELET TRANSFORM

Authors

  • B.Swetha Student of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, Andhra pradesh, India
  • M.Vasantha Student of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, Andhra pradesh, India
  • SK.Salma Student of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, Andhra pradesh, India
  • K.Sravani Student of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, Andhra pradesh, India

Keywords:

Doubly-fed induction generator (DFIG), fault diagnosis, rotor current, support vector machine (SVM), Wind turbine, Continuous Wavelet Transform, MATLAB.

Abstract

Fault diagnosis of DFIG is a prominent challenge in wind turbine condition monitoring. Many machine
learning algorithms have been applied to DFIG fault diagnosis. However, these current machine learning algorithms
failed to give satisfactory fault diagnosis results due to some of their drawbacks. This paper presents a wavelet
transform and support vector machine (SVM) technique based algorithm for fault diagnosis of DFIG. The SVM is
used to extract the information from a signal over a wide range of frequencies. This analysis is performed in both time
and frequency domains. Experimental results are validated by using MATLAB.

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Published

2019-05-31

How to Cite

B.Swetha, M.Vasantha, SK.Salma, & K.Sravani. (2019). FAULT DIAGNOSIS OF DFIG USING WAVELET TRANSFORM. International Journal of Technical Innovation in Modern Engineering & Science, 5(15), -. Retrieved from https://ijtimes.com/index.php/ijtimes/article/view/3133

Issue

Vol. 5 No. 15 (2019): NCITEE-2019 (National Conference on Innovative Technologies in Electrical Engineering)

Section

Articles