Abstract

This paper proposes an approach for noninvasively fault detection in a wide range of power electronics applications. Simulations were used at this stage for gathering the large amount of data needed, but the conclusions can be applied as-is to experimental data sets. These data were fed to the machine-learning algorithm for training and validation. A buck converter based on silicon-carbide MOSFETs is selected as the case study. The beauty of the proposed approach is that it only uses the output terminal signals for diagnostics. Our approach starts with extracting features from the output voltage signal, then inferring criteria to evaluate and rank these features in terms of relevance (feature engineering phase). The final step in this phase is reducing the space dimension by selecting sets of meaningful features. Finally, several artificial neural network structures demonstrate the effectiveness of this approach.
OriginalsprogEngelsk
TitelProceedings of the 2021 IEEE Energy Conversion Congress and Exposition (ECCE)
Antal sider7
ForlagIEEE Press
Publikationsdatookt. 2021
Sider3652-3658
Artikelnummer9595152
ISBN (Trykt)978-1-7281-6128-0
ISBN (Elektronisk)978-1-7281-5135-9
DOI
StatusUdgivet - okt. 2021
Begivenhed2021 IEEE Energy Conversion Congress and Exposition (ECCE) - Vancouver, BC, Canada
Varighed: 10 okt. 202114 okt. 2021

Konference

Konference2021 IEEE Energy Conversion Congress and Exposition (ECCE)
LokationVancouver, BC, Canada
Periode10/10/202114/10/2021
NavnIEEE Energy Conversion Congress and Exposition
ISSN2329-3721

Fingeraftryk

Dyk ned i forskningsemnerne om 'An Application of Feature Engineering and Machine Learning Algorithms on Condition Monitoring of SiC Converters'. Sammen danner de et unikt fingeraftryk.

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