Machine learning based fault classification approach for power electronic converters

V. S. Bharath Kurukuru, Ahteshamul Haque, Rajesh Kumar, Mohammed Ali Khan, Arun Kumar Tripathy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

This research proposes a fault classification approach for power electronics converters (PECs) operating with the distributed generation systems. The proposed approach classifies and localizes the faults in PECs by adapting wavelet transforms and artificial neural networks. Initially, various failure mechanisms for power modules in PECs are identified to generate the fault data. Further, the detailed and approximate coefficients of the fault data at each frequency band are extracted using wavelet transform and used as inputs to the classifier. The ANN classifier estimates the non-linear relationship between the features and targets patterns to develop the fault classification mechanism. The numerical simulations are carried out for injecting various faults and degradation scenarios in both the legs of the inverter and developing the fault classifier in Plecs/MATLAB integration. The results showed 97.4% training accuracy with component failure classifier and 94.2% training accuracy with the classifier trained for 50% degradation in power module.

Original languageEnglish
Title of host publication9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728156729
DOIs
Publication statusPublished - 16 Dec 2020
Externally publishedYes
Event9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020 - Jaipur, India
Duration: 16 Dec 202019 Dec 2020

Publication series

Name9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020

Conference

Conference9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
Country/TerritoryIndia
CityJaipur
Period16/12/2019/12/20

Keywords

  • Artificial neural network (ANN)
  • Distributed generation
  • Fault classification
  • Power electronics converter (PEC)
  • Wavelet transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Automotive Engineering

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