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FDI oriented modeling of an experimental SOFC system, model validation and simulation of faulty states

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  • Sorce, A.
  • Greco, A.
  • Magistri, L.
  • Costamagna, P.

Abstract

We present here a model we have established for a laboratory-size SOFC system. Model equations are presented and discussed, together with an extensive model validation, carried out in both steady state and transient operating conditions. The validated model is then used to simulate four classes of system faults, i.e. air leakage, fuel leakage, SOFC degradation and reformer degradation. When the different faults occur, the physico-chemical operating parameters of the system vary in a different manner, and our main objective is to understand their behavior. For example, for the system under analysis, SOFC degradation and fuel leakage cause a decrease of the gas discharge temperature, while reformer degradation causes an increase of the same parameter. The results of the fault analysis are described and discussed, and are reported in such a way to provide a basis for the development of an FDI tool based on pattern recognition techniques.

Suggested Citation

  • Sorce, A. & Greco, A. & Magistri, L. & Costamagna, P., 2014. "FDI oriented modeling of an experimental SOFC system, model validation and simulation of faulty states," Applied Energy, Elsevier, vol. 136(C), pages 894-908.
    • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:894-908
    DOI: 10.1016/j.apenergy.2014.03.074
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    Cited by:

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    3. Fardadi, Mahshid & McLarty, Dustin F. & Jabbari, Faryar, 2016. "Investigation of thermal control for different SOFC flow geometries," Applied Energy, Elsevier, vol. 178(C), pages 43-55.
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    5. van Biert, L. & Visser, K. & Aravind, P.V., 2020. "A comparison of steam reforming concepts in solid oxide fuel cell systems," Applied Energy, Elsevier, vol. 264(C).
    6. van Biert, L. & Godjevac, M. & Visser, K. & Aravind, P.V., 2019. "Dynamic modelling of a direct internal reforming solid oxide fuel cell stack based on single cell experiments," Applied Energy, Elsevier, vol. 250(C), pages 976-990.
    7. Zhang, Zehan & Li, Shuanghong & Xiao, Yawen & Yang, Yupu, 2019. "Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning," Applied Energy, Elsevier, vol. 233, pages 930-942.
    8. Wang, Baoxuan & Zhu, Jiang & Lin, Zijing, 2016. "A theoretical framework for multiphysics modeling of methane fueled solid oxide fuel cell and analysis of low steam methane reforming kinetics," Applied Energy, Elsevier, vol. 176(C), pages 1-11.
    9. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).

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