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Research Papers

Fault Diagnosis of Solid Oxide Fuel Cell Based on a Supervised Self-Organization Map Model

[+] Author and Article Information
XiaoJuan Wu

School of Automation,
University of Electronic Science and Technology of China,
Chengdu 610054, China
e-mail: xj2_wu@ hotmail.com

Hongtan Liu

Clean Energy Research Institute,
College of Engineering,
University of Miami,
Coral Gables, FL 33146

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received November 26, 2013; final manuscript received October 31, 2014; published online January 28, 2015. Assoc. Editor: Ellen Ivers-Tiffée.

J. Fuel Cell Sci. Technol 12(3), 031001 (Jun 01, 2015) (8 pages) Paper No: FC-13-1112; doi: 10.1115/1.4029070 History: Received November 26, 2013; Revised October 31, 2014; Online January 28, 2015

Too high stack temperature and insufficient reactant gas flow may lead to severe and irreversible damages in a real solid oxide fuel cell (SOFC) power system. Thus, fault monitoring and diagnosis technology is indispensable to improve the SOFC system reliability. A supervised self-organization map (SOM) model is proposed to diagnose the faults of the SOFC system in this paper. Using the supervised SOM model, the multidimensional testing data of the SOFC is mapped into a two-dimensional map, and the different region in the out map is represented for one fault mode. The method is evaluated using the data obtained from an SOFC mathematical model, and the results show that the supervised SOM analysis contributes on a very efficient way to the faults diagnosis of the SOFC system.

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References

Figures

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Fig. 1

Structure diagram of SOFC system

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Fig. 2

SOFC system model developed in matlab

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Fig. 3

Temperature control architecture

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Fig. 4

Evolution of variables by fault in the temperature controller: (a) SOFC stack voltage, (b) SOFC stack power, (c) SOFC stack temperature, and (d) compressor power

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Fig. 5

Evolution of variables by air leakage fault: (a) SOFC stack voltage, (b) SOFC stack power, (c) SOFC stack temperature, and (d) compressor power

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Fig. 6

Evolution of variables by two faults: (a) SOFC stack voltage, (b) SOFC stack power, (c) SOFC stack temperature, and (d) compressor power

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Fig. 7

Supervised SOM topology

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Fig. 8

Classified results of training samples

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Fig. 9

Classified results of testing samples

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