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Fault Diagnosis for IGBTs Open-Circuit Faults in Photovoltaic Grid-Connected Inverters Based on Statistical Analysis and Machine Learning
ID：163 View Protection：PUBLIC Updated Time：2020-10-22 19:50:21 Hits：712 Poster Presentation

Start Time：2020-11-04 16:05（Asia/Shanghai）

Duration：5min

Session：G Poster session » G2Poster Session 2 and Poster Session 7

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Abstract
A new fault diagnosis method for IGBTs open-circuit faults based on statistical analysis and machine learning is proposed to improve the reliability of photovoltaic power generation system. Firstly, empirical mode decomposition (EMD) is used to realize the adaptive filtering of the noise in three-phase current. Secondly, statistical analysis and generalized discriminant analysis (GDA) are used for feature extraction and feature dimensionality reduction. Then, BP neural network is used for fault pattern recognition. Finally, the rapidity and accuracy of the proposed method are verified by simulation experiments. At the same time, the proposed method is compared with the traditional feature extraction method based on fast Fourier transform (FFT) and EMD and principal component analysis (PCA)-based dimension reduction method. The results show that the proposed method has high fault recognition rate and simple neural network topology.
Keywords
BP neural network;EMD noise reduction;GDA;inverter;open-circuit fault diagnosis
Speaker
Hongyu Long
HeFei University of Technology

Submission Author
Hongyu Long HeFei University of Technology；School of Electrical Engineering and Automation
Mingyao Ma HeFei University of Technology；School of Electrical Engineering and Automation
Weisheng Guo HeFei University of Technology；School of Electrical Engineering and Automation
Fei Li HeFei University of Technology；School of Electrical Engineering and Automation
Xing Zhang HeFei University of Technology；School of Electrical Engineering and Automation
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Important Date

  • Conference Date

    Nov 02

    2020

    to

    Nov 04

    2020

  • Oct 27 2020

    Draft paper submission deadline

  • Nov 03 2020

    Contribution Submission Deadline

  • Nov 04 2020

    Registration deadline

  • Nov 17 2020

    Final Paper Deadline

Sponsored By
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
Organized By
Huazhong University of Science and Technology
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