Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch

Zhanjun Huang; Zhanshan Wang; Huaguang Zhang

doi:10.1109/JAS.2017.7510496

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 177-185

Zhanjun Huang, Zhanshan Wang and Huaguang Zhang, "Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 177-185, Apr. 2017. doi: 10.1109/JAS.2017.7510496

Citation:

Zhanjun Huang, Zhanshan Wang and Huaguang Zhang, "Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 177-185, Apr. 2017. doi: 10.1109/JAS.2017.7510496

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Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch

doi: 10.1109/JAS.2017.7510496

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China, and also with the State Key Laboratory of Synthetical Automation for Process Industries, Shenyang 110819, China

Funds:

This work was supported by National Natural Science Foundation of China 61473070

This work was supported by National Natural Science Foundation of China 61433004

Fundamental Research Funds for the Central Universities N130504002

SAPI Fundamental Research Funds 2013ZCX01

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Author Bio:
Zhanshan Wang received the M. Sc. degree in control theory and control engineering from Fushun Petroleum Institute (now Liaoning Shihua University), Fushun, China, in 2001, and the Ph. D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2006. His research interests include stability analysis of recurrent neural networks, fault diagnosis, fault tolerant control, nonlinear control theory and their applications in smart grid. Since 2010, he has been a professor with Northeastern University in China. He has authored and coauthored over 110 journal and conference papers and five monographs. He is the holder of 10 patents. Dr. Wang was an associate editor of IEEE Transaction on Neural Networks and Learning Systems. Now he is an associate editor of Acta Automatica Sinica(zhanshan wang@163.com)

Huaguang Zhang received the B. S. and M. S. degrees in control engineering from Northeastern Dianli University, Jilin, China, in 1982 and 1985, respectively, and the Ph. D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a post-doctoral fellow for two years. Since 1994, he has been a professor and the head of the Institute of Electric Automation, School of Information Science and Engineering, Northeastern University. He has authored and coauthored over 280 journal and conference papers, 6 monographs, and coinvented 90 patents. His current research interests include fuzzy control, stochastic control, neural networks-based control, nonlinear control, and their industrial applications. Dr. Zhang was the chair of the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of the IEEE Computational Intelligence Society. He is an associate editor of Automatica, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and Neurocomputing. He was an associate editor of IEEE Transactions on Fuzzy Systems from 2008 to 2013. He was a recipient of the Outstanding Youth Science Foundation Award from the National Natural Science Foundation of China in 2003 and the IEEE Transactions on Neural Networks Outstanding Paper Award in 2012. He was named the Cheung Kong Scholar by the Education Ministry of China in 2005.(hgzhang@ieee.org)
Corresponding author: Zhanjun Huang received the B. Sc. degree in communication engineering and M. Sc. degree in electrical theory and new technology from Northeastern University, Shenyang, China, in 2012 and 2014, respectively. He is currently working toward the Ph. D. degree in power electronics and power transmission at the College of Information Science and Engineering, Northeast University, Shenyang, China. His current research interests include data processing and analysis, machine learning, neural networks, stability analysis of dynamical systems, and their applications in power electronics. Corresponding author of this paper.(e-mail: zj huang@sina.com)
Received Date: 2015-11-02
Accepted Date: 2016-07-18

Abstract

Abstract

Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network (ANN) classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore, according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current. Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied. Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results.
- Absolute average ratio process,
- fault diagnosis,
- microgrid inverter,
- multilevel feature moving average ratio,
- neural network

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References

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Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch

doi: 10.1109/JAS.2017.7510496

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