Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features

Lei Kou, Chuang Liu, Guo-wei Cai, Zhe Zhang, Jia-ning Zhou, Xue-mei Wang

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, the system does not crash suddenly, the performance will be reduced for instance voltages fluctuation and current harmonics. A fault diagnosis method based on deep feedforward network with transient synthetic features is proposed to reduce the dependence on the fault mathematical models in this paper, which mainly uses the transient phase current to train the deep feedforward network classifier. Firstly, the features of fault phase current are analyzed in this paper. Secondly, the historical fault data after feature synthesis is employed to train the deep feedforward network classifier, and the average fault diagnosis accuracy can reach 97.85% for transient synthetic fault data, the classifier trained by the transient synthetic features obtained more than 1% gain in performance compared with original transient features. Finally, the online fault diagnosis experiments show that the method can accurately locate the fault IGBTs, and the final diagnosis result is determined by multiple groups results, which has the ability to increase the accuracy and reliability of the diagnosis results.

Original languageEnglish
JournalISA Transactions
Volume101
Pages (from-to)399-407
ISSN0019-0578
DOIs
Publication statusPublished - 2020

Keywords

  • Deep feedforward network
  • Fault diagnosis
  • Open-circuit fault in IGBT
  • Three-phase PWM rectifier
  • Transient synthetic features

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