Vibrant fault diagnosis for hydroelectric generator unit considering overlapping fault patterns

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control

Volumn 40, Issue 3, 2012, Pages 8-14

  Zhang, Xiao Yuan ^a   Zhou, Jian Zhong ^a   Wang, Chang Qing ^a   Li, Chao Shun ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fault diagnosis; Hydroelectric generator unit; Overlapping fault patterns; Rough sets; Support vector machine

Indexed keywords

CLASSIFICATION RESULTS; FAULT PATTERNS; FAULT SAMPLE; HYBRID METHOD; LOWER APPROXIMATION; MULTICLASS SVM; OVERLAPPING PATTERNS; POWER EQUIPMENT; ROUGH SET; SUPPORT VECTOR; UPPER APPROXIMATION;

FAILURE ANALYSIS; HYDROELECTRIC GENERATORS; ROUGH SET THEORY;

SUPPORT VECTOR MACHINES;

EID: 84863291468     PISSN: 16743415     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (22)

1. MA Zhen-yue, DONG Yu-xin. Vibration and its corrective actions of water turbine generator set and power house [M]. Beijing: China Water Power Press, 2004.
2. FU Xiang-qian, JIANG Jin, SUN Mu-qun, et al. Application of fuzzy diagnosis to fault diagnosis system of hydraulic generator sets [J]. Journal of Huazhong University of Science and Technology: Nature Science, 2006, 34(1): 81-83.
3. LIU Zhong, ZHOU Jian-zhong, ZHANG Yong-chuan, et al. Compound characteristic extraction based fault diagnosis of hydroelectric generating unit using RBF neural network [J]. Automation of Electric Power Systems, 2007, 31(11): 87-91.
4. CHEN Lin-gang, HAN Feng-qin, GUI Zhong-hua. A neural network based intelligent fault diagnosis system for hydroelectric generating sets [J]. Power System Technology, 2006, 30(1): 40-43, 98.
5. LAN F, LI J H. Hydroelectric generating sets fault diagnosis based on DDAGSVM [C]//3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, Nanjing, PEOPLES R CHINA, 2008: 679-683.
6. PENG Wen-ji, LUO Xing-qi, GUO Peng-cheng, et al. Vibration fault diagnosis of hydroelectric unit based on LS-SVM and information fusion technology [J]. Proceedings of the CSEE, 2007, 27(23): 86-92.
7. PENG Wen-ji, LUO Xing-qi. Vibration fault diagnosis of hydro-turbine generating unit based on rough sets and support vector machine [J]. Transaction of China Electrotechnical Society, 2006, 21(10): 117-122.
8. GAO Cai-liang, LIAO Zhi-wei, YUE Ling, et al. Fault diagnosis of HV transmission lines based on wavelet singular value and support vector machine [J]. Power System Protection and Control, 2010, 38(6): 35-39, 51.
9. WANG Cheng-jiang, MA Xin-ming, GUAN Yun, et al. An improved binary tree classifier for transmission line fault based on SVM [J]. Power System Protection and Control, 2010, 38(5): 39-44.
10. HUA Bin, ZHOU Jian-zhong, YU Jing. Application of Bayesian networks in fault diagnosis of hydroelectric unit [J]. Journal of North China Electric Power University, 2004, 31(5): 33-36.
11. ZHANG Xiao-dan, ZHAO Hai, XIE Yuan-mang, et al. Bayesian network model for fault diagnosis of hydropower equipment [J]. Journal of Northeastern University: Natural Science, 2006, 27(3): 276-279.
12. HE Xiao-fei, TONG Xiao-yang, ZHOU Shu, et al. Power system fault diagnosis based on Bayesian network and fault section location [J]. Power System Protection and Control, 2010, 38(12): 29-34.
13. ZHANG Xiao-yuan, ZHOU Jian-zhong, HUANG Zhi-wei, et al. Vibrant fault diagnosis for hydro-turbine generating unit based on rough sets and multi-class support vector machine [J]. Proceedings of the CSEE, 2010, 30(20): 88-93.
14. Tang Y H, Gao J H. Improved classification for problem involving overlapping patterns [J]. 2007, E90D(11): 1787-1795.
15. Lingras Pawan, Butz Cory. Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification. Information Sciences [J]. 2007, 177(18): 3782-3798.
16. Paul P, Ilan W, William S. Support vector machine classification on the web [J]. Bioinformatics, 2004, 20(4): 586-587.
17. Blake C L, Merz C J. (1998) UCI repository of machine learning databases. University of California Department Information and Computer Science, Irvine, CA [EB/OL]. http://www.ics.uci.edu/mlearn/MLRepository.html.
18. Shigeo Abe, Keita Sakaguchi. Generalization improvement of a fuzzy classifier with ellipsodial regions [C]//FUZZ-IEEE 2001: 207-210.
19. SHEN Dong, CHU Fu-tao, CHEN Si. Diagnosis and Identification of Vibration Accident for Hydrogenerator Unit [J]. Journal of Hydrodynamics, 2000, 15(1): 129-133.
20. PENG Wen-Ji, LUO Xing-qi, ZHAO Dao-li. Vibrant fault diagnosis of hydro-turbine generating unit based on spectrum analysis and RBF network method [J]. Proceedings of the CSEE, 2006, 26(9): 155-158.
21. WANG Rong-rong. Research on fault diagnosis of hydroelectric unit vibration based on variable precision rough set and neural network [D]. Xi'an: Xi'an University of Technology, 2007.
22. Jia R, Huang G. Hydroelectric generating unit vibration fault diagnosis via BP neural network based on particle swarm optimization [C]//International Conference on Sustainable Power Generation and Supply, 2009. 6-7 April, 2009.