Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

被引：9

作者：

Xue, Tao ^{[1
]}

Wu, Xiaolong ^{[1
]}

Xu, Yuanwu ^{[1
]}

Jing, Suwen ^{[1
]}

Li, Zehua ^{[1
]}

Jiang, Jianhua ^{[1
]}

Deng, Zhonghua ^{[1
]}

Fu, Xiaowei ^{[2
]}

Xi, Li ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Educ Minist Image Proc & Intelligent Control, Key Lab, Wuhan 430074, Hubei, Peoples R China

[2] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan 430065, Hubei, Peoples R China

来源：

INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS | 2019年 / 158卷

基金：

中国国家自然科学基金;

关键词：

SOFC; fault diagnosis; neural network;

D O I：

10.1016/j.egypro.2019.01.423

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：1798 / 1803

页数：6

共 9 条

[1]

Arsie I., 2010, P ASME 2010 8 INT FU

[2] Analysis of Leakages in a Solid Oxide Fuel Cell Stack in a System Environment [J].