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Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model

Author

Listed:
  • Zhong, Xiaobo
  • Xu, Yuanwu
  • Liu, Yanlin
  • Wu, Xiaolong
  • Zhao, Dongqi
  • Zheng, Yi
  • Jiang, Jianhua
  • Deng, Zhonghua
  • Fu, Xiaowei
  • Li, Xi

Abstract

Solid oxide fuel cell system is a energy conversion device with the advantages of low emissions, high efficiency and long life. However, the occurrence and propagation of oscillations are common in a system. When certain variables oscillate, the lifetime of system is significantly reduced and the output electrical characteristics are affected. Therefore, it is important to analyze and diagnose the root cause of solid oxide fuel cell system oscillations to prevent the propagation of the oscillations. An independent solid oxide fuel cell system consists of multiple subsystems, and each subsystem consists of multiple process variables. It is not easy to locate the root cause of the oscillations accurately. A combination of data-driven causality and topology-based model is adopted in this paper, which provides a complete procedure for diagnosing system oscillations. First, the method of combining principal component analysis and oscillation significance index is chosen to select feature variables. Then the data-driven Granger causality analysis is applied to provide reliable diagnosis of oscillation source. Finally, the diagnosis result is further enhanced by topology-based model which takes process connectivity and knowledge into account. Through analysis and system experiment, the source of oscillations Feed CH4 PV is successfully found. The result shows that the method based on the combination of data and topology model can accurately locate the root cause of oscillations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920304803
    DOI: 10.1016/j.apenergy.2020.114968
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    References listed on IDEAS

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    Cited by:

    1. Gallo, Marco & Costabile, Carmine & Sorrentino, Marco & Polverino, Pierpaolo & Pianese, Cesare, 2020. "Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems," Applied Energy, Elsevier, vol. 279(C).
    2. Karim Nadim & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 57-83, January.
    3. Santosh B. Rane & Sandesh Wavhal & Prathamesh R. Potdar, 2023. "Integration of Lean Six Sigma with Internet of Things (IoT) for productivity improvement: a case study of contactor manufacturing industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1990-2018, October.
    4. 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.

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