Fault Diagnosis of Fuel Leakage in Solid Oxide Fuel Cell System

被引:0
作者
Yu, Longkun [1 ]
Long, Zhengyang [1 ]
Yan, Weijian [1 ]
Zhong, Yunsheng [1 ]
Hu, Lingyan [1 ]
Wu, Xiaolong [1 ]
机构
[1] Nanchang Univ, Coll Informat Engn, Nanchang, Jiangxi, Peoples R China
来源
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
基金
中国国家自然科学基金;
关键词
decision tree; solid oxide fuel cell system; fault diagnosis; OPERATION;
D O I
10.1109/CCDC58219.2023.10326571
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fuel cell technology is the fourth generation technology after hydropower, thermal power and nuclear power. Once the solid oxide fuel cell system fails, if it can't be found in time, the initial glitch may slowly evolve and spread to the subsequent components. Therefore, fault diagnosis is a prerequisite to ensure its stability. In order to diagnose fuel leakage fault of solid oxide fuel cell system, a decision tree is proposed to diagnose the fuel leakage fault of solid oxide fuel cell. Compared with other machine learning methods, it can be clearly observed that the decision tree method can effectively identify the severity of faults. This method can be extended to the fault diagnosis of air leakage.
引用
收藏
页码:3596 / 3600
页数:5
相关论文
共 16 条
  • [1] Anode-supported intermediate-temperature direct internal reforming solid oxide fuel cell - II. Model-based dynamic performance and control
    Aguiar, P
    Adjiman, CS
    Brandon, NP
    [J]. JOURNAL OF POWER SOURCES, 2005, 147 (1-2) : 136 - 147
  • [2] Progress in solid oxide fuel cell-gas turbine hybrid power systems: System design and analysis, transient operation, controls and optimization
    Azizi, Mohammad Ali
    Brouwer, Jacob
    [J]. APPLIED ENERGY, 2018, 215 : 237 - 289
  • [3] Cano J. R., 2018, MONOTONIC CLASSIFICA, V341, P168
  • [4] Dynamic modeling and experimental validation for the electrical coupling in a 5-cell solid oxide fuel cell stack in the perspective of thermal coupling
    Cao, Hongliang
    Li, Xi
    Deng, Zhonghua
    Jiang, Jianhua
    Yang, Jie
    Li, Jian
    Qin, Yi
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2011, 36 (07) : 4409 - 4418
  • [5] Fan J. M., 2020, INT J ELECT POST ENE, V11
  • [6] SOFC Detector With OCA Approach to Quantify Trace Gases Dissolved in Transformer Oil
    Fan, Jingmin
    Lu, Yuanshen
    Ding, Jiafeng
    Meng, Anbo
    Tang, Zhenhua
    Ye, Jiazhuo
    [J]. IEEE SENSORS JOURNAL, 2020, 20 (02) : 648 - 655
  • [7] Frank F., 2001, P 12 EUR C MACH LEAR
  • [8] A FUZZY K-NEAREST NEIGHBOR ALGORITHM
    KELLER, JM
    GRAY, MR
    GIVENS, JA
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1985, 15 (04): : 580 - 585
  • [9] Kim Sunhee, 2018, PERFORMANCE ANAL BIO
  • [10] Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach
    Lan, Liang
    Wang, Zhuang
    Zhe, Shandian
    Cheng, Wei
    Wang, Jun
    Zhang, Kai
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (02) : 369 - 378