[Poster Presentation]Fault Diagnosis for IGBTs Open-Circuit Faults in Photovoltaic Grid-Connected Inverters Based on Statistical Analysis and Machine Learning

Fault Diagnosis for IGBTs Open-Circuit Faults in Photovoltaic Grid-Connected Inverters Based on Statistical Analysis and Machine Learning

ID：163 Submission ID：1545 View Protection：PUBLIC Updated Time：2020-10-22 19:50:21 Hits：193 Poster Presentation

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

Duration：5min

Session：[G] Poster session » [G2] Poster Session 2 and Poster Session 7

Video Play Presentation File Attachment File

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