Fast Fault Diagnosis Method of Evaporatively Cooled Fuel Cell System Based on Online Sequential Extreme Learning Machine and Principal Component Analysis
Liu Jiawei, Li Qi, Chen Weirong, Yu Jiaxi, Yan Yu
School of Electrical Engineering Southwest Jiaotong University Chengdu 611756 China
Abstract In order to solve the fault diagnosis problem of evaporatively cooled fuel cell system, a novel fast fault diagnosis method for evaporatively cooled fuel cell system was proposed based on online sequential extreme learning machine and principal component analysis. The principal component analysis was used to filter the redundant information and obtain the fault eigenvectors that could reflect the states of evaporatively cooled fuel cell system. The fault feature vectors were classified by online sequential extreme learning machine, thereby effectively improving the diagnostic accuracy of the model and reducing the computation time. The case analysis shows that the novel method can quickly identify three healthy states of membrane drying failure, hydrogen leakage failure, and normal state. The diagnostic accuracy of the algorithm is 99.67% and the computation time is 0.296 9 seconds. The diagnostic accuracy of the novel method is 14.34% and 9.34% higher than those of SVM and BPNN respectively, and the computation time of online sequential extreme learning machine is only 1/1 011 of SVM and 1/132 of BPNN. Therefore, the proposed method is suitable for online fault diagnosis of evaporatively cooled fuel cell system with large data samples and multi-data dimensions.
Liu Jiawei,Li Qi,Chen Weirong等. 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.
[1] 陈维荣, 刘嘉蔚, 李奇, 等. 质子交换膜燃料电池故障诊断方法综述及展望[J]. 中国电机工程学报, 2017, 37(16): 4712-4721. Chen Weirong, Liu Jiawei, Li Qi, et al.Review and prospect of fault diagnosis methods for proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2017, 37(16): 4712-4721. [2] 赵思臣, 王奔, 谢玉洪, 等. 无外增湿质子交换膜燃料电池线性温度扫描实验[J]. 中国电机工程学报, 2014, 34(26): 4528-4533. Zhao Sichen, Wang Ben, Xie Yuhong, et al.Linear temperature sweep experimental study on proton exchange membrane fuel cell without external humidification[J]. Proceedings of the CSEE, 2014, 34(26): 4528-4533. [3] 杨德友, 崔冬晓, 蔡国伟. 基于云智能控制器的燃料电池最大功率跟踪策略[J]. 电工技术学报, 2018, 33(14): 3362-3370. Yang Deyou, Cui Dongxiao, Cai Guowei.A maximum power point tracking technology for fuel cells using cloud model based intelligent controller[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3362-3370. [4] 游志宇, 刘涛, 史青, 等. 空冷自增湿质子交换膜燃料电池发电控制器设计[J]. 电工技术学报, 2018, 33(2): 442-450. You Zhiyu, Liu Tao, Shi Qing, et al.Design of generation controller of air-cooled self-humidifying proton exchange membrane fuel cell[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 442-450. [5] 魏立明, 吕雪莹. 固体氧化物燃料电池发电系统模型建立及逆变器仿真研究[J]. 电力系统保护与控制, 2016, 44(24): 37-43. Wei Liming, Lü Xueying.Solid oxide fuel cell power generation system model and study on inverter simulation[J]. Power System Protection and Control, 2016, 44(24): 37-43. [6] Liu Jianxing, Luo Wensheng, Yang Xiaozhan, et al.Robust model-based fault diagnosis for PEM fuel cell air-feed system[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3261-3270. [7] 孔令国, 蔡国伟, 李龙飞, 等. 风光氢综合能源系统在线能量调控策略与实验平台搭建[J]. 电工技术学报, 2018, 33(14): 3371-3384. Kong Lingguo, Cai Guowei, Li Longfei, et al.Online energy control strategy and experimental platform of integrated energy system of wind, photovoltaic and hydrogen[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3371-3384. [8] 蔡国伟, 陈冲, 孔令国, 等. 风电/制氢/燃料电池/超级电容器混合系统控制策略[J]. 电工技术学报, 2017, 32(17): 84-94. Cai Guowei, Chen Chong, Kong Lingguo, et al.Control of hybrid system of wind/hydrogen/fuel cell/ supercapacitor[J]. Transactions of China Electro- technical Society, 2017, 32(17): 84-94. [9] 郑先娜, 林瑞全. 质子交换膜燃料电池控制策略综述[J]. 电气技术, 2017, 18(11): 1-5. Zhen Xianna, Lin Ruiquan.Summarization of control strategy for proton exchange membrane fuel cell[J]. Electrical Engineering, 2017, 18(11): 1-5. [10] Petrone R, Zheng Zhixue, Hissel D, et al.A review on model-based diagnosis methodologies for PEMFCs[J]. International Journal of Hydrogen Energy, 2013, 38(17): 7077-7091. [11] Zheng Zhixue, Petrone R, Péra M C, et al.A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems[J]. International Journal of Hydrogen Energy, 2013, 38(21): 8914-8926. [12] 刘嘉蔚, 李奇, 陈维荣, 等. 基于多分类相关向量机和模糊C均值聚类的有轨电车用燃料电池系统故障诊断方法[J]. 中国电机工程学报, 2018, 38(20): 6045-6052. Liu Jiawei, Qi Li, Chen Weirong, et al.Fault diagnosis method of fuel cell system for tramway based on multi-class relevance vector machine and fuzzy C means clustering[J]. Proceedings of the CSEE, 2018, 38(20): 6045-6052. [13] Zhao Xingwang, Xu Liangfei, Li Jianqiu, et al.Faults diagnosis for PEM fuel cell system based on multi- sensor signals and principle component analysis method[J]. International Journal of Hydrogen Energy, 2017, 42(29): 18524-18531. [14] 周苏, 韩秋玲, 胡哲, 等. 质子交换膜燃料电池故障诊断的模式识别方法[J]. 同济大学学报: 自然科学版, 2017, 45(3): 408-412. Zhou Su, Han Qiuling, Hu Zhe, et al.Pattern recognition method for proton exchange membrane fuel cell fault diagnosis[J]. Journal of Tongji University: Natural Science, 2017, 45(3): 408-412. [15] Mao Lei, Jackson L, Davies B.Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis[J]. IEEE Transactions on Industrial Electronics, 2018, 65(9): 7301-7310. [16] Li Zhongliang, Outbib R, Giurgea S, et al.Fault diagnosis for PEMFC systems in consideration of dynamic behaviors and spatial inhomogeneity[J]. IEEE Transactions on Energy Conversion, 2018, DOI: 10.1109/TEC.2018.2824902. [17] Zheng Zhixue, Morando S, Pera M C, et al.Brain- inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack[J]. International Journal of Hydrogen Energy, 2017, 42(8): 5410-5425. [18] Liang Nanying, Huang Guangbin, Saratchandran P, et al.A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006, 17(6): 1411-1423. [19] 张秦梫, 宋辉, 姜勇, 等. 基于OS-ELM的变压器局部放电模式识别[J]. 高电压技术, 2018, 44(4): 1122-1130. Zhang Qinqin, Song Hui, Jiang Yong, et al.Partial discharge pattern recognition of transformer based on OS-ELM[J]. High Voltage Engineering, 2018, 44(4): 1122-1130. [20] 李业波, 李秋红, 王健康, 等. 基于ImOS-ELM的航空发动机传感器故障自适应诊断技术[J]. 航空学报, 2013, 34(10): 2316-2324. Li Yebo, Li Qiuhong, Wang Jiankang, et al.Sensor fault adaptive diagnosis of aero-engines based on ImOS-ELM[J]. Acta Aeronautica ET Astronautica Sinica, 2013, 34(10): 2316-2324. [21] Qian Peng, Ma Xiandong, Zhang Daihai.Estimating health condition of the wind turbine drivetrain system[J]. Energies, 2017, 10(10): 1583. [22] 王宏力, 何星, 陆敬辉, 等. 基于固定尺寸序贯极端学习机的模拟电路在线故障诊断[J]. 仪器仪表学报, 2014, 35(4): 738-744. Wang Hongli, He Xing, Lu Jinghui, et al.Analog circuit online fault diagnosis based on fix-size sequence extreme learning machine[J]. Chinese Journal of Scientific Instrument, 2014, 35(4): 738-744. [23] Mao Lei, 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. [24] 彭跃进, 张国瑞, 王勇, 等. 阴、阳极加湿对质子交换膜燃料电池性能影响的差异性[J]. 电工技术学报, 2017, 32(4): 196-203. Peng Yuejin, Zhang Guorui, Wang Yong, et al.Differences on the influences of humidity of cathod and anode on the performance of proton exchange membrane fuel cell[J]. Transactions of China Elec- trotechnical Society, 2017, 32(4): 196-203. [25] 罗悦齐, 张嵩, 高丽萍, 等. 质子交换膜燃料电池低温启动水热管理特性及优化[J]. 电工技术学报, 2018, 33(11): 2626-2635. Luo Yueqi, Zhang Song, Gao Liping, et al.Optimization of water and thermal management in proton exchange membrane fuel cell during low temperature startup[J]. Transactions of China Elec- trotechnical Society, 2018, 33(11): 2626-2635. [26] Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al.Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2012, 42(2): 513-529. [27] Huang Guangbin, Chen Lei, Siew C K.Universal approximation using incremental constructive feed- forward networks with random hidden nodes[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892. [28] 齐敏芳, 付忠广, 景源, 等. 基于信息熵与主成分分析的火电机组综合评价方法[J]. 中国电机工程学报, 2013, 33(2): 58-64. Qi Minfang, Fu Zhongguang, Jing Yuan, et al.A comprehensive evaluation method of power plant units based on information entropy and principal component analysis[J]. Proceedings of the CSEE, 2013, 33(2): 58-64.
2019, Vol. 34 
