Data-driven fault diagnosis-tolerant control integrated technique for solid oxide fuel cell

Author(s):Yilin Wanga,b , Shuanghong Lia,b*, Yupu Yanga,b
a Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, PR China
b Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, PR China
International Journal of Smart Grid and Clean Energy, vol. 9, no. 2, March 2020: pp. 357-365
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.9.2.357-365

Abstract: Fault monitoring and diagnosis system are very important in detecting system failures and keeping the stability of production line for the modern industrial system. This paper studies the data-driven fault diagnosis and tolerant control integrated technique focused on Solid Oxide Fuel Cell (SOFC) system, especially for the stack degradation fault. The multivariable statistical approach Support Vector Machine (SVM) as well as Principal Component Analysis (PCA) are studied for the multi-fault classification and diagnosis purpose. Then based on the diagnosis results, the decision-making part is designed to select appropriate fault reconfigurable control strategy which can handle five types of stack faults and recover the thermal and electrical parameters to normal operating condition. The core of integrated data-based fault monitoring and control approach is to take full advantage of available SOFC measurements data aiming to acquire the useful fault condition information and choosing adequate fault recovery control method. The results are that the proposed strategy can keep the system power near the normal operating state and make the SOFC temperature steady near the enactment value when working in faulty conditions, which may result in both lifetime and durability improvement for the SOFC system.

Keywords: Fault diagnosis, SOFC, fault tolerant control, PCA, SVM
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