Fault Diagnosis for Power Converters based on Random Forests and Feature Transformation

Lei Kou, Chuang Liu, Guo-wei Cai, Zhe Zhang, Xue-jiao Li, Quan-de Yuan

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Abstract

Power converters have been key enablers of many energy conversion fields, and it is a trend to apply artificial intelligent (AI) technology to power converters to improve stability. A novel fault diagnosis method based on the combination of random forests (RFs) and feature transformation is proposed in this paper. Firstly, the three-phase AC fault currents of threephase PWM rectifier are analyzed as examples. Secondly, the feature transformation, a novel current trajectories slopes based method, is adopted to transform the fault currents data. With the help of feature transformation, the fault diagnosis classifier can obtain a good load adaptability. And then the RFs based method, a data-driven method, is employed to train the fault diagnosis
classifier with the fault current trajectories slopes data. Finally, the proposed method is carried out on an three-phase PWM rectifier system, which can detect and locate the open-circuit faults of IGBTs.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE 9th International Power Electronics and Motion Control Conference
PublisherIEEE
Pages1821-1826
Publication statusAccepted/In press - 2021
Event2020 IEEE 9th International Power Electronics and Motion Control Conference - International Youth Cultural Centre, Nanjing, China
Duration: 29 Nov 20202 Dec 2020

Conference

Conference2020 IEEE 9th International Power Electronics and Motion Control Conference
LocationInternational Youth Cultural Centre
Country/TerritoryChina
CityNanjing
Period29/11/202002/12/2020

Keywords

  • Fault diagnosis
  • Power converters
  • Data-driven
  • Random forests
  • Feature transformation
  • Current trajectories slopes

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