Data-Driven Parameter Fault Classification for A DC–DC Buck Converter

Yichuan Fu*, Zhiwei Gao*, Aihua Zhang

*Corresponding author for this work

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

2 Citations (Scopus)


DC–DC power converters play an important role in renewable energy systems, electrical vehicles, and battery chargers and so forth. DC–DC Buck converters are prone to faults due to age and unexpected accidents. As a result, there is a high demand to improve the operation reliability and safety of power converters by using condition monitoring and fault diagnosis techniques. In this paper, data-driven and machine learning-based fault detection and fault classification strategies are addressed for DC–DC Buck converters under disparate faulty scenarios of the parameters. A variety of algorithms such as principal component analysis, multi-linear principal component analysis, uncorrelated multi-linear principal component analysis, and Fast Fourier Transformation pre-processing based multi-linear principal component analysis and uncorrelated multi-linear principal component analysis techniques are applied for fault classification and diagnosis of the parameter faults in the DC–DC Buck converters. The effectiveness is demonstrated and discussed with details.
Original languageEnglish
Title of host publicationProceedings of the 2021 6th International Symposium on Environment Friendly Energies and Applications (EFEA
EditorsRadostina A. Angelova, Rositsa Velichkova
Place of PublicationPiscataway
Number of pages7
ISBN (Electronic)9781728170114, 9781728170107
Publication statusPublished - 24 Mar 2021
EventEFEA 2021: Are you ready to change the world? - Technical University of Sofia, Sofia, Bulgaria
Duration: 24 Mar 202126 Mar 2021

Publication series

Name2021 6th International Symposium on Environment-Friendly Energies and Applications (EFEA)
ISSN (Electronic)2688-2558


ConferenceEFEA 2021
Internet address


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