Soft Fault Diagnosis for DC-DC Converters with Wavelet Transform and Fuzzy Cerebellar Model Neural Networks

Zipeng Han, Qiongbin Lin, Zhe Zhang

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Abstract

Identifying the soft faults of converters in power electronic converter is a significant problem for the stable and efficient operation of power systems. This paper proposed a novel soft fault diagnosis method based on wavelet transform and fuzzy cerebellar model neural networks (WT-FCMNN) for DC-DC
Converters. First, the multiscale feature extraction is achieved by multilevel signal decomposition to extract the feature information of different frequency ranges signal. Meanwhile, optimal wavelet decomposition scale and feature dimension reduction are used to reduce computational quantity and eliminate redundant information. Then, in order to effectively diagnose soft faults in DC-DC converters, a classifier based on FCMNN is proposed to identify different operating states of the capacitor and power MOSFETs in push-pull circuits. Finally, two common fault diagnosis methods and the proposed FCMNN are performed for circuit fault diagnosis. Compared with the BPNN and SVM, simulation results show that the proposed method has a better generalization, fast diagnosis speed and higher diagnostic accuracy that proves its effectiveness and feasibility in soft fault diagnosis.
Original languageEnglish
Title of host publicationProceedings of IEEE 9th International Power Electronics and Motion Control Conference
PublisherIEEE
Pages1811-1815
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
CountryChina
CityNanjing
Period29/11/202002/12/2020

Bibliographical note

2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia)

Keywords

  • Soft fault diagnosis
  • Wavelet transform
  • Fuzzy cerebellar model neural networks
  • DC-DC converter

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