Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Fault Diagnosis Method of Three-phase Inverter Based on Time Convolutional Neural Network

Author(s): Gang Li*, Jing Li, Yizhuo He and Qun Guo

Volume 15, Issue 5, 2022

Published on: 15 August, 2022

Page: [401 - 409] Pages: 9

DOI: 10.2174/2352096515666220617112310

Price: $65

Abstract

Power electronics converter is widely used in various applications. When the converter failure occurs, the system will be damaged, so it is of great significance to timely detect the fault and apply effective protection. In this paper, the open-circuit fault of the three-phase inverter is taken as the main object. The time convolutional neural network (TCN) model is designed to realize the fault diagnosis strategy of the inverter based on feature extraction. The simulated experimental results show that the neural network model can realize high accuracy fault diagnosis for different inverter circuits. The overall classification performance is good, and the loss is small. It is shown that the time convolutional neural network method can diagnose different IGBT fault intelligently in the inverter. It is proved that the neural network model is effective and feasible for inverter circuit fault diagnosis.

Background: Power electronics converter is widely used in various applications. When the converter failure occurs, the system will be damaged, so it is of great significance to timely detect the fault and apply effective protection.

Objective: The open-circuit fault of the three-phase inverter is taken as the main object.

Method: The time convolutional neural network (TCN) model is designed to realize the fault diagnosis strategy of the inverter based on feature extraction.

Results: The simulated experimental results show that the neural network model can realize high accuracy fault diagnosis for different inverter circuits. The overall classification performance is good, and the loss is small.

Conclusion: It is shown that the time convolutional neural network method can diagnose different IGBT fault intelligently in the inverter. It has been proved that the neural network model is effective and feasible for inverter circuit fault diagnosis.

Keywords: Three-phase inverter, TCN, fault diagnosis, IGBT, open-circuit, feature extraction.

« Previous Next »
Graphical Abstract

[1]
Y. Lv, Research on Fault Diagnosis Method based on Stack Self-Coding Network., Zhejiang University, 2019.
[2]
H. Ren, Q.U Jianfeng, Y. Chai, Q. Tang, and X. Ye, "A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system", Neurocomputing, vol. 275, no. 08, pp. 2111-2125, 2018.
[3]
L. Wang, "Research on fault diagnosis of three-level inverter based on adaptive neural fuzzy inference system", In., China University of Mining and Technology, 2015.
[4]
X. Chen, H. Lu, and G. Liu, "Analysis and simulation of single-phase rectifier/inverter h-bridge", Electr. Drive Autom., vol. 34, no. 05, pp. 1-7, 2012.
[5]
R. Peng, J. Rong, Y. Liu, J. Liu, and S. Zhu, "Modeling and simulation of three-phase voltage inverter circuit in MATALB", Electron. Tech., vol. 44, no. 02, pp. 10-12, 2015.
[6]
Y. Wang, Bearing Fault Diagnosis of Rotary Kiln Reducer based on Convolutional Neural Network., Tianjin Polytechnic University, 2020.
[7]
G. Yating, W. Wu, L. Qiongbin, C. Fenghuang, and C. Qinqin, "Fault diagnosis for power converters based on optimized temporal convolutional network", IEEE Trans. Instrum. Meas., vol. 70, pp. 1-10, 2021.
[http://dx.doi.org/10.1109/TIM.2020.3021110]
[8]
M. Wei, Research on Traffic Flow Prediction Method of Urban Expressway Based On Time Convolution Network., Beijing Jiaotong University, 2020.
[9]
S. Bai, J.Z. Kolter, and V. Koltun, "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling", arXiv preprint arXiv:1803.01271, 2018.
[10]
Y. Xia, Y. Xu, and B. Gou, "A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification", IEEE Trans. Industr. Inform., vol. 16, no. 8, pp. 5223-5233, 2020.
http://home.process.com/Intranets/wp2.htp [http://dx.doi.org/10.1109/TII.2019.2949344]
[11]
Z. Li, Y. Gao, and X. Zhang, "A model-data-hybrid-driven diagnosis method for open-switch faults in power converters", IEEE Trans. Power Electron., vol. 09, p. 1, 2020.
[12]
H. Liang, X.Y. Luo, Z.Y. Wang, and J. Liang, "Representation learning via a semi-supervised stacked distance autoencoder for image classification", Front Inform. Technol. Electron. Eng., vol. 21, no. 7, pp. 1005-1018, 2020.
[13]
Z. Zhang, Fault Detection and Concurrent Fault Recognition Based on Automatic Coding Representation Learning., Shanghai Jiaotong University, 2019.
[14]
C. Zhang, Y. He, and D. Bolun, "Intelligent fault diagnosis method for power transformer based on deep learning", J. Electron. Meas. Instrum., vol. 34, no. 1, pp. 81-89, 2020.
[15]
C.N. Ibem, M.E. Farrag, and A.A. Aboushady, "New fuzzy logic based switch-fault diagnosis in three phase inverters", In55th International Universities Power Engineering Conference (UPEC), 1-4, Sept, 2020, Turin, Italy, 2020, pp. 1-6.
[16]
J. Yang, Q. Pan, and H. Pan, "A bacterial light response reveals an orphan desaturase for human plasmalogen synthesis", Science, vol. 42, no. 05, pp. 128-132, 2019.
[17]
F. Yuying, L. Chengjuan, Y. Qiang, and L. Baoqing, "Field movement object classification based on improved TCN network J/OL", Comput. Eng., 2022, pp. 1-12.

Rights & Permissions Print Cite
Article Metrics
13
2
© 2024 Bentham Science Publishers | Privacy Policy