Fault diagnosis of power electronic system based on fault gradation and neural network group

Neurocomputing

Volumn 72, Issue 13-15, 2009, Pages 2909-2914

  Ma, Chengcai ^a   Gu, Xiaodong ^a   Wang, Yuanyuan ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Fault diagnosis; Fault gradation; Neural network group

Indexed keywords

BACKPROPAGATION; COMPLEX NETWORKS; FAILURE ANALYSIS; FAULT TOLERANT COMPUTER SYSTEMS; NEURAL NETWORKS; ONLINE SYSTEMS;

BP (BACK PROPAGATION) NEURAL NETWORK; BP NEURAL NETWORKS; COMPLEX FAULT DIAGNOSIS; FAULT DIAGNOSIS SYSTEMS; FAULT GRADATION; MECHANICAL SYSTEMS; NEURAL NETWORK GROUP; POWER ELECTRONIC SYSTEMS;

FAULT DETECTION;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CONTROLLED STUDY; ELECTRIC CONDUCTIVITY; ELECTRIC CURRENT; ELECTRIC MODEL; INTEGRATED CIRCUIT; LEARNING ALGORITHM; MOLECULAR ELECTRONICS; ONLINE SYSTEM; POWER SUPPLY; PRIORITY JOURNAL; PROCESS MODEL; QUALITY CONTROL; SYSTEMATIC ERROR;

EID: 78149404128     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.06.033     Document Type: Article

References (14)

1. M. Polycarpou, T. Vemuri, Learning methodology for failure detection and accommodation, IEEE Control Systems (1995) 16-24.
2. K. Kumamaru, T. Soderstrom, Fault detection and model validation using index of Kullback discrimination information, Transactions on the Society of Instrument and Control Engineers 22 (10) (1986) 1135-1140.
3. M.H. Wang, A novel extension neural network for power transformer fault diagnosis, IEE Proceedings-Generation, Transmission and Distribution 150 (6) (2003) 669-685.
4. J.L. Guardado, J.L. Naredo, P. Moreno, et al., A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis, IEEE Transactions on Power Delivery 16 (4) (2001) 643-647.
5. B. Yang, H. Ding, Expert system of transformer fault diagnosis based on knowledge base, Proceedings of the Chinese Society for Electrical Engineering 22 (10) (2002) 122-124.
6. E.H. Francis, L.X. Shen, Fault diagnosis based on rough set theory, Engineering Applications of Artificial Intelligence 16 (1) (2003) 39-43.
7. M. Emesto Vazquez, L. Oscar, M. Chacon, J. Hector, F. Altuve, An on line expert system for fault section diagnosis in power system, IEEE Transactions on Power Systems 12 (1) (1997) 357-362.
8. C.S. Chang, J.M. Chen, D. Srinivasan, F.S. Wen, A.C. Liew, Fuzzy logic approach in power system fault section identification, IEE Proceedings-Generation, Transmission and Distribution 144 (5) (1997) 406-414.
9. C.S. Chang, L. Tian, F.S. Wen, A new approach to fault section estimation in power systems using ant system, Electric Power System Research (41) (1999) 63-70.
10. Y.M. Park, G.-W. Kim, A logic based expert system (LBES) for fault diagnosis of power system, IEEE Transactions on Power Systems 12 (1) (1997) 363-369.
11. V. Navarro, A.L. da Silva, L.A.V. de Carvalho, G. Zaverucha, Artificial neural networks for power systems diagnosis, in: 1994 IEEE International Conference on Neural Networks, 1994, pp. 3738-3743.
12. Z.E. Aygen, S. Seker, M. Bagriyanik, F.G. Bagriyanik and E. Ayaz, Fault section estimation in electrical power systems using artificial neural network approach, in: 1999 IEEE Transmission and Distribution Conference, 1999, pp. 466-469.
13. S. Hayashi, T. Asakura, S. Zhang, Study of machine fault diagnosis system using neural networks, in: International Joint Conference on Neural Networks (IJCNN '02), 2002, pp. 956-961.
14. X. Dehong, C. Zhaoji, C. Xuejun, Fault diagnosis of power electronic circuits with Qscrete Fourier transform, Journal of Zhejiang University 28 (6) (1994) 666-673.