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

Mingfei Li, Zhengpeng Chen, Jiangbo Dong, Kai Xiong, Chuangting Chen, Mumin Rao, Zhiping Peng, Xi Li and Jingxuan Peng
Additional contact information
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
Jingxuan Peng: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 7, 1-16

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.

Keywords: SOFC; fault diagnosis; BP neural network; the reformer carbon deposition; heat exchanger rupture (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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