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Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment

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  • Wu, Xiao-long
  • Xu, Yuan-Wu
  • Xue, Tao
  • Zhao, Dong-qi
  • Jiang, Jianhua
  • Deng, Zhonghua
  • Fu, Xiaowei
  • Li, Xi

Abstract

For the distributed household generation technology, the operation status identification and prediction of multiple startup and shutdown solid oxide fuel cell (SOFC) system are of great significance to the optimization of power generation efficiency and long-term operation. The existing SOFC system research focuses on traditional physical models rather than actual SOFC system, which makes the system research too idealized and ignores the actual problems, such as health state prediction, voltage fluctuation and failure analysis of key equipment such as heat exchanger. Therefore, based on the data-driven actual natural gas SOFC system, this paper builds Elman neural network state prediction model under the entire stage (including system start-stop, long-term operation, hot-standby) to predict the future voltage, and infers the potential fault information of the system from the residual voltage. On this basis, combined with the dynamic response of the system, the essential causes of voltage fluctuation and heat exchanger breakage of the actual SOFC system are found out. And the improvement scheme of the system is proposed. The results show that the typical faults of the SOFC system can be accurately identified by combining Elman neural network state prediction model with multivariable dynamic response analysis of SOFC system.

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  • 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.
    • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:126-140
    DOI: 10.1016/j.apenergy.2019.04.053
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    8. Deng, Zhihua & Chen, Qihong & Zhang, Liyan & Zong, Yi & Zhou, Keliang & Fu, Zhichao, 2020. "Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems," Applied Energy, Elsevier, vol. 277(C).
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    11. 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.
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    13. Rahaf S. Ghanem & Laura Nousch & Maria Richter, 2022. "Modeling of a Grid-Independent Set-Up of a PV/SOFC Micro-CHP System Combined with a Seasonal Energy Storage for Residential Applications," Energies, MDPI, vol. 15(4), pages 1-20, February.
    14. Mumin Rao & Li Wang & Chuangting Chen & Kai Xiong & Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Junli Xu & Xi Li, 2022. "Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method," Energies, MDPI, vol. 15(9), pages 1-15, April.
    15. 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.
    16. 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).
    17. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    18. 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).
    19. Wang, Yang & Wu, Chengru & Zhao, Siyuan & Wang, Jian & Zu, Bingfeng & Han, Minfang & Du, Qing & Ni, Meng & Jiao, Kui, 2022. "Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell," Applied Energy, Elsevier, vol. 315(C).
    20. 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.
    21. 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.
    22. 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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