Study of Open Circuit Fault Diagnosis in VSI using Discrete Wavelet Transform and Machine Learning Approach

  • Vaishali Sonawane, Sanjay B. Patil

Abstract

Fault Diagnosis (FD) is becoming a complex process with the advancement of technology. The three-phase voltage source inverters (VSIs) motor drive experiences troubles caused by different faults like short circuit fault, gate misfiring fault, and open circuit fault. The faults need to be identified eventually, diagnosed appropriately based on a dedicated diagnosis approach. In most of the FD methods, person interference is unavoidable. This is one of the inspiring features of exploring the abilities of Artificial Intelligence in the area of FD. The key contributions of the paper are to analyze the time-frequency domain characteristics of output current waveforms at healthy and faulty conditions using a Discrete Wavelet Transform technique. Extract statistical features such as skewness, kurtosis, min, max, mean, and entropy from the phase current output of the inverter using DWT. To locate and identify the open circuit fault in VSI using different Machine Learning algorithms and do a comparative study.

Published
2020-03-30
How to Cite
Vaishali Sonawane, Sanjay B. Patil. (2020). Study of Open Circuit Fault Diagnosis in VSI using Discrete Wavelet Transform and Machine Learning Approach. International Journal of Advanced Science and Technology, 29(3), 13370 -. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/31538
Section
Articles