IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2556-d784608.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2556/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Polverino, Pierpaolo & Sorrentino, Marco & Pianese, Cesare, 2017. "A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems," Applied Energy, Elsevier, vol. 204(C), pages 1198-1214.
    3. 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).
    4. 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).
    5. Gabriele Moser & Paola Costamagna & Andrea De Giorgi & Andrea Greco & Loredana Magistri & Lissy Pellaco & Andrea Trucco, 2015. "Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, May.
    6. Sharaf, Omar Z. & Orhan, Mehmet F., 2014. "An overview of fuel cell technology: Fundamentals and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 810-853.
    7. Kirubakaran, A. & Jain, Shailendra & Nema, R.K., 2009. "A review on fuel cell technologies and power electronic interface," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2430-2440, December.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.
    3. Yuanwu Xu & Hao Shu & Hongchuan Qin & Xiaolong Wu & Jingxuan Peng & Chang Jiang & Zhiping Xia & Yongan Wang & Xi Li, 2022. "Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter," Energies, MDPI, vol. 15(7), pages 1-17, March.
    4. 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).
    5. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    6. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. Marcus Evandro Teixeira Souza Junior & Luiz Carlos Gomes Freitas, 2022. "Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    8. Mohammed, Hanin & Al-Othman, Amani & Nancarrow, Paul & Tawalbeh, Muhammad & El Haj Assad, Mamdouh, 2019. "Direct hydrocarbon fuel cells: A promising technology for improving energy efficiency," Energy, Elsevier, vol. 172(C), pages 207-219.
    9. Zhao, Qiankun & Cai, Ximing & Mischo, William & Ma, Liyuan, 2020. "How do the research and public communities view biofuel development?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    10. Xia, Zhiping & Zhao, Dongqi & Li, Yuanzheng & Deng, Zhonghua & Kupecki, Jakub & Fu, Xiaowei & Li, Xi, 2023. "Control-oriented dynamic process optimization of solid oxide electrolysis cell system with the gas characteristic regarding oxygen electrode delamination," Applied Energy, Elsevier, vol. 332(C).
    11. Das, Vipin & Padmanaban, Sanjeevikumar & Venkitusamy, Karthikeyan & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Siano, Pierluigi, 2017. "Recent advances and challenges of fuel cell based power system architectures and control – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 10-18.
    12. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    13. Shi, Jihao & Zhang, Xinqi & Zhang, Haoran & Wang, Qiliang & Yan, Jinyue & Xiao, Linda, 2024. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning," Applied Energy, Elsevier, vol. 361(C).
    14. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    15. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    16. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    17. Gallo, Marco & Polverino, Pierpaolo & Mougin, Julie & Morel, Bertrand & Pianese, Cesare, 2020. "Coupling electrochemical impedance spectroscopy and model-based aging estimation for solid oxide fuel cell stacks lifetime prediction," Applied Energy, Elsevier, vol. 279(C).
    18. Koushik Ahmed & Omar Farrok & Md Mominur Rahman & Md Sawkat Ali & Md Mejbaul Haque & Abul Kalam Azad, 2020. "Proton Exchange Membrane Hydrogen Fuel Cell as the Grid Connected Power Generator," Energies, MDPI, vol. 13(24), pages 1-20, December.
    19. Zhao, Chen & Li, Baozhu & Zhang, Lu & Han, Yaru & Wu, Xiaoyu, 2023. "Novel optimal structure design and testing of air-cooled open-cathode proton exchange membrane fuel cell," Renewable Energy, Elsevier, vol. 215(C).
    20. Wang, Yujie & Sun, Zhendong & Li, Xiyun & Yang, Xiaoyu & Chen, Zonghai, 2019. "A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems," Energy, Elsevier, vol. 189(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2556-:d:784608. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.