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A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems

Author

Listed:
  • Mingfei Li

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Zhengpeng Chen

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Jiangbo Dong

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Kai Xiong

    (Guangdong Energy Group Co., Ltd., Guangzhou 510630, China)

  • Chuangting Chen

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Mumin Rao

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Zhiping Peng

    (Guangdong Huizhou Lng Power Co., Ltd., Huizhou 516081, China)

  • Xi Li

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518055, China)

  • Jingxuan Peng

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

Abstract

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2556-:d:784608
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    References listed on IDEAS

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