IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v267y2020ics0306261920304803.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920304803
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114968?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Azizi, Mohammad Ali & Brouwer, Jacob, 2018. "Progress in solid oxide fuel cell-gas turbine hybrid power systems: System design and analysis, transient operation, controls and optimization," Applied Energy, Elsevier, vol. 215(C), pages 237-289.
    3. Perna, A. & Minutillo, M. & Jannelli, E. & Cigolotti, V. & Nam, S.W. & Han, J., 2018. "Design and performance assessment of a combined heat, hydrogen and power (CHHP) system based on ammonia-fueled SOFC," Applied Energy, Elsevier, vol. 231(C), pages 1216-1229.
    4. Yan, Dong & Zhang, Chi & Liang, Linjiang & Li, Kai & Jia, Lichao & Pu, Jian & Jian, Li & Li, Xi & Zhang, Tao, 2016. "Degradation analysis and durability improvement for SOFC 1-cell stack," Applied Energy, Elsevier, vol. 175(C), pages 414-420.
    5. 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.
    6. 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.
    7. 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.
    8. Jiang, Jianhua & Shen, Tan & Deng, Zhonghua & Fu, Xiaowei & Li, Jian & Li, Xi, 2018. "High efficiency thermoelectric cooperative control of a stand-alone solid oxide fuel cell system with an air bypass valve," Energy, Elsevier, vol. 152(C), pages 13-26.
    9. Yan, Min & Zeng, Min & Chen, Qiuyang & Wang, Qiuwang, 2012. "Numerical study on carbon deposition of SOFC with unsteady state variation of porosity," Applied Energy, Elsevier, vol. 97(C), pages 754-762.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Tilocca, Giuseppe & Sánchez, David & Torres-García, Miguel, 2024. "Applying the root cause analysis methodology to study the lack of market success of micro gas turbine systems," Applied Energy, Elsevier, vol. 360(C).
    3. 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.
    4. 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.
    5. 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.

    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. Xiao-Long Wu & Hong Zhang & Hongli Liu & Yuan-Wu Xu & Jingxuan Peng & Zhiping Xia & Yongan Wang, 2022. "Modeling Analysis of SOFC System Oriented to Working Condition Identification," Energies, MDPI, vol. 15(5), pages 1-19, February.
    3. 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.
    4. Wu, Xiao-long & Xu, Yuan-wu & Zhao, Dong-qi & Zhong, Xiao-bo & Li, Dong & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Extended-range electric vehicle-oriented thermoelectric surge control of a solid oxide fuel cell system," Applied Energy, Elsevier, vol. 263(C).
    5. 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.
    6. Jingxuan Peng & Dongqi Zhao & Yuanwu Xu & Xiaolong Wu & Xi Li, 2023. "Comprehensive Analysis of Solid Oxide Fuel Cell Performance Degradation Mechanism, Prediction, and Optimization Studies," Energies, MDPI, vol. 16(2), pages 1-23, January.
    7. Guk, Erdogan & Venkatesan, Vijay & Babar, Shumaila & Jackson, Lisa & Kim, Jung-Sik, 2019. "Parameters and their impacts on the temperature distribution and thermal gradient of solid oxide fuel cell," Applied Energy, Elsevier, vol. 241(C), pages 164-173.
    8. 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.
    9. 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.
    10. Cheng, Tianliang & Jiang, Jianhua & Wu, Xiaodong & Li, Xi & Xu, Mengxue & Deng, Zhonghua & Li, Jian, 2019. "Application oriented multiple-objective optimization, analysis and comparison of solid oxide fuel cell systems with different configurations," Applied Energy, Elsevier, vol. 235(C), pages 914-929.
    11. Singh, Surinder P. & Ohara, Brandon & Ku, Anthony Y., 2021. "Prospects for cost-competitive integrated gasification fuel cell systems," Applied Energy, Elsevier, vol. 290(C).
    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. 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).
    14. Jie, Hao & Liao, Jiawei & Zhu, Guozhu & Hong, Weirong, 2024. "Nonlinear model predictive control of direct internal reforming solid oxide fuel cells via PDAE-constrained dynamic optimization," Applied Energy, Elsevier, vol. 360(C).
    15. Subotić, Vanja & Stoeckl, Bernhard & Lawlor, Vincent & Strasser, Johannes & Schroettner, Hartmuth & Hochenauer, Christoph, 2018. "Towards a practical tool for online monitoring of solid oxide fuel cell operation: An experimental study and application of advanced data analysis approaches," Applied Energy, Elsevier, vol. 222(C), pages 748-761.
    16. 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.
    17. 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.
    18. 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).
    19. 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).
    20. 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.

    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:eee:appene:v:267:y:2020:i:c:s0306261920304803. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.