FAULT DIAGNOSIS METHOD OF PEMFC SYSTEM BASED ON P-L DUAL FEATURE EXTRACTION

He Fei; Zhang Xuexia; Chen Weirong

doi:10.19912/j.0254-0096.tynxb.2022-1488

PDF(2161 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 492-499. DOI: 10.19912/j.0254-0096.tynxb.2022-1488

FAULT DIAGNOSIS METHOD OF PEMFC SYSTEM BASED ON P-L DUAL FEATURE EXTRACTION

He Fei, Zhang Xuexia, Chen Weirong

Author information +

History +

Abstract

For the fault diagnosis of proton exchange membrane fuel cell （PEMFC） system, a fault diagnosis method based on P-L dual feature extraction was proposed. P-L dual feature extraction is used to extract features from the preprocessed sample data. Through redundant variable removal and secondary feature extraction, classification features are preserved to the maximum extent and the dimension of sample data is effectively reduced. Binary tree multi-class support vector machine and extreme learning machine are used to classify 2D fault feature vectors and realize fault diagnosis. Through the example verification, compared with the feature extraction effect of linear discriminant analysis, P-L dual feature extraction improves the diagnostic accuracy of the test set of the same classifier by 21.19%, and the diagnostic accuracy reaches 99.27%, realizing the accurate and rapid diagnosis of membrane dry and hydrogen supply faults in PEMFC system.

Key words

proton exchange membrane fuel cell（PEMFC） / fault detection / data mining / P-L dual feature extraction / support vector machine（SVM） / extreme learning machine（ELM）

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

He Fei, Zhang Xuexia, Chen Weirong. FAULT DIAGNOSIS METHOD OF PEMFC SYSTEM BASED ON P-L DUAL FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 492-499 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1488

References

[1] 周伟, 何华东. 浅谈质子交换膜燃料电池耐久性的研究现状[J]. 科技信息, 2010(26): 522-523.
ZHOU W, HE H D.Research status of durability of proton exchange membrane fuel cells[J]. Science & technology information, 2010(26): 522-523.
[2] 张雪霞, 蒋宇, 黄平, 等. 质子交换膜燃料电池容错控制方法综述[J]. 中国电机工程学报, 2021, 41(4): 1431-1444, 1549.
ZHANG X X, JIANG Y, HUANG P, et al.A review of fault-tolerant control methodology on proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2021, 41(4): 1431-1444, 1549.
[3] ZHANG X X, ZHOU J Z, CHEN W R.Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning[J]. International journal of hydrogen energy, 2020, 45(24): 13483-13495.
[4] 张雪霞, 高雨璇, 陈维荣. 基于数据驱动的质子交换膜燃料电池寿命预测[J]. 西南交通大学学报, 2020, 55(2): 417-427.
ZHANG X X, GAO Y X, CHEN W R.Data-driven based remaining useful life prediction for proton exchange membrane fuel cells[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 417-427.
[5] ZHANG X X, YU Z X, CHEN W R.Life prediction based on D-S ELM for PEMFC[J]. Energies, 2019, 12(19): 3752.
[6] ALLAM A, MANGOLD M, ZHANG P.Fault diagnosis of proton exchange membrane fuel cells[C]//2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol). Saint-Raphael, France, 2021: 366-371.
[7] LI Z, GIURGEA S, OUTBIB R, et al.Online diagnosis of PEMFC by combining support vector machine and fluidic model[J]. Fuel cells, 2014, 14(3): 448-456.
[8] LI Z L, OUTBIB R, HISSEL D, et al.Online diagnosis of PEMFC by analyzing individual cell voltages[C]//2013 European Control Conference(ECC). Zurich, Switzerland, 2013: 2439-2444.
[9] LI Z L, OUTBIB R, GIURGEA S, et al.Fault detection and isolation for polymer electrolyte membrane fuel cell systems by analyzing cell voltage generated space[J]. Applied energy, 2015, 148: 260-272.
[10] 刘嘉蔚, 李奇, 陈维荣, 等. 基于概率神经网络和线性判别分析的PEMFC水管理故障诊断方法研究[J]. 中国电机工程学报, 2019, 39(12): 3614-3622.
LIU J W, LI Q, CHEN W R, et al.Research on PEMFC water management fault diagnosis method based on probabilistic neural network and linear discriminant analysis[J]. Proceedings of the CSEE, 2019, 39(12): 3614-3622.
[11] MAO L, JACKSON L, DUNNETT S.Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches[J]. Fuel cells, 2017, 17(2): 247-258.
[12] 张雪霞, 蒋宇, 孙腾飞, 等. 质子交换膜燃料电池水淹和膜干故障诊断研究综述[J]. 西南交通大学学报, 2020, 55(4): 828-838, 864.
ZHANG X X, JIANG Y, SUN T F, et al.Review on fault diagnosis for flooding and drying in proton exchange membrane fuel cells[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 828-838, 864.
[13] 梁栋, 侯明, 窦美玲, 等. 质子交换膜燃料电池燃料饥饿现象[J]. 电源技术, 2010, 34(8): 767-770.
LIANG D, HOU M, DOU M L, et al.Study on behavior of proton exchange membrane fuel cell under fuel starvation conditions[J]. Chinese journal of power sources, 2010, 34(8): 767-770.
[14] 刘嘉蔚, 李奇, 陈维荣, 等. 基于在线序列超限学习机和主成分分析的蒸汽冷却型燃料电池系统快速故障诊断方法[J]. 电工技术学报, 2019, 34(18): 3949-3960.
LIU J W, LI Q, CHEN W R, et al.Fast fault diagnosis method of evaporatively cooled fuel cell system based on online sequential extreme learning machine and principal component analysis[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3949-3960.
[15] HUANG G B, ZHOU H M, DING X J, et al.Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems, man, and cybernetics Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, 2012, 42(2): 513-529.