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Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

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  • Xiaowei Fu

    (College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
    Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
    State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yanlin Liu

    (College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
    Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China)

  • Xi Li

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The solid oxide fuel cell (SOFC) is a new energy technology that has the advantages of low emissions and high efficiency. However, oscillation and propagation often occur during the power generation of the system, which causes system performance degradation and reduced service life. To determine the root cause of multi-loop oscillation in an SOFC system, a data-driven diagnostic method is proposed in this paper. In our method, kernel principal component analysis (KPCA) and transfer entropy were applied to the system oscillation fault location. First, based on the KPCA method and the Oscillation Significance Index (OSI) of the system process variable, the process variables that were most affected by the oscillations were selected. Then, transfer entropy was used to quantitatively analyze the causal relationship between the oscillation variables and the oscillation propagation path, which determined the root cause of the oscillation. Finally, Granger causality (GC) analysis was used to verify the correctness of our method. The experimental results show that the proposed method can accurately and effectively locate the root cause of the SOFC system’s oscillation.

Suggested Citation

  • Xiaowei Fu & Yanlin Liu & Xi Li, 2020. "Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven," Energies, MDPI, vol. 13(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4069-:d:395281
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    References listed on IDEAS

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    1. He, Yongxiu & Pang, Yuexia & Zhang, Qi & Jiao, Zhe & Chen, Qian, 2018. "Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory," Renewable Energy, Elsevier, vol. 122(C), pages 643-653.
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    3. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    4. Xiaojun Song & Abderrahim Taamouti, 2019. "A Better Understanding of Granger Causality Analysis: A Big Data Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 911-936, August.
    5. Wu, Xiao-long & Xu, Yuan-Wu & Xue, Tao & Zhao, Dong-qi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2019. "Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment," Applied Energy, Elsevier, vol. 248(C), pages 126-140.
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    Cited by:

    1. Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Kai Xiong & Chuangting Chen & Mumin Rao & Zhiping Peng & Xi Li & Jingxuan Peng, 2022. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems," Energies, MDPI, vol. 15(7), pages 1-16, March.

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