Transformer fault diagnosis using self-adaptive RBF neural network algorithm

Gaodianya Jishu/High Voltage Engineering

Volumn 38, Issue 6, 2012, Pages 1368-1375

  Fu, Qiang ^a   Chen, Tefang ^a   Zhu, Jiaojiao ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Dissolved gas analysis(DGA); Fault diagnosis; Fuzzy C means(FCM) algorithm; Gaussian distribution particle swarm optimization(PSO) algorithm; Self adaptive radial basis function(RBF) neural network; Transformer

Indexed keywords

DISSOLVED GAS ANALYSIS; FUZZY C-MEANS ALGORITHMS; PARTICLE SWARM OPTIMIZATION ALGORITHM; RADIAL BASIS FUNCTION NEURAL NETWORKS; TRANSFORMER;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); ELECTRIC TRACTION; FAILURE ANALYSIS; FUZZY SYSTEMS; GAUSSIAN DISTRIBUTION; NEURAL NETWORKS; PROGRAM PROCESSORS; RADIAL BASIS FUNCTION NETWORKS; STATISTICAL TESTS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84864515116     PISSN: 10036520     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1003-6520.2012.06.013     Document Type: Article

References (25)

1. FU Qiang, CHEN Tefang. Design and implementation of a digital comprehensive test system for electric locomotive traction transformer [J]. Computer Measurement & Control, 2008, 16(1): 49-51.
2. CHEN Weigen, PAN Chong, YUN Yuxin, et al. Fault diagnostic method for power transformer based on improved wavelet neural network algorithm [J]. Chinese Journal of Scientific Instrument, 2008, 28(7): 1489-1493.
3. Ganyun L V, Cheng Haozhong, Zhai Haibao, et al. Fault diagnosis of power transformer based on multilayer SVM classifier [J]. Electric Power System Research, 2005, 74(1): 1-7.
4. ZHU Yongli, WU Lizeng, LI Xueyu, et al. Synthesized diagnosis on transformer faults based on Bayesian classifier and rough set [J]. Proceedings of the CSEE, 2005, 25(10): 159-165.
5. FU Yang, TIAN Zhenning, JIANG Yurong, et al. Power transformer fault diagnosis using weighted fuzzy kernel clustering [J]. High Voltage Engineering, 2010, 36(2): 371-374.
6. CHEN Xingang, TIAN Xiaoxiao, ZHAO Yangyang, et al. Application of information fusion to recognition of partial discharge in transformer [J]. High Voltage Engineering, 2012, 38(3): 553-559.
7. HU Qing, SUN Caixin, DU Lin, et al. Transformer fault diagnosis method using random forests and kernel principle component analysis [J]. High Voltage Engineering, 2010, 36(7): 1725-1729.
8. FEI Shengwei, MIAO Yubin, LIU Chengliang, et al. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine [J]. High Voltage Engineering, 2009, 35(3): 509-513.
9. HUANG Huixian, XIAO Guizhi, YANG Min. Power transformer fault diagnosis using RST and NBN [J]. High Voltage Engineering, 2009, 35(7): 1589-1594.
10. CHANG Tao, ZHANG Xiaoxing, XIONG Hao, et al. Dissolved gas analysis in transformer oil using dynamic tunneling fuzzy C-means algorithm [J]. High Voltage Engineering, 2009, 35(9): 2181-2184.
11. Piggio T, Girosi F. A theory of networks for approximation and learning [R]. Cambridge, UK: Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge Massachusetts, 1989.
12. XIE Zhenping, LIU Jihong, WANG Shitong. RBF network learning algorithm using robust least-squares [J]. Control and Decision, 2010, 25(4): 502-506.
13. Peng Jianxun, Li Kang, Irwin George W. A novel continuous forward algorithm for RBF neural modeling [J]. IEEE Transactions on Automatic Control, 2007, 52(1): 117-122.
14. SUN Jian, SHEN Ruimin, HAN Peng. An original RBF network learning algorithm [J]. Chinese Journal of Computers, 2003, 26(1): 1562-1567.
15. Chiang Junghsien, Ho Shinghua. A combination of rough-based feature selection and RBF neural network for classification using gene expression data [J]. IEEE Transactions on Nan-Bioscience, 2008, 7(1): 91-99.
16. LIU Jun, SHANG Xiuqin, LU Jiangang, et al. RBF design method based on center clustering and PSO [J]. Computer Engineering and Application, 2009, 45(36): 212-216.
17. Ke Meng, Zhao Yangdong, Dian Huiwang, et al. A Self-adaptive RBF neural network classifier for transformer fault analysis [J]. IEEE Transactions on Power Systems, 2010, 25(3): 1350-1360.
18. Asuncion A, Newman D J. UCI machine learning repository [OB/OL]. http://www.ics.uci.edu/~mlearn/MLRepository.html.
19. Moody J, Darken C. Learning with localized receptive fields [C]//1988 Proceedings of Connectionist Models Summer School. California, USA: [s.n.], 1988: 133-143.
20. Moody J, Darken C. Fast learning in networks of locally-tuned processing units [J]. Neural Computation, 1989, 1(2): 281-294.
21. YU Jian. On the fuzziness index of the FCM algorithms [J]. Chinese Journal of Computers, 2003, 26(8): 968-973.
22. Kennedy J, Eberhart R C. Particle swarm optimization [C]//Proceedings of the IEEE International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
23. Krinkt, Vesterstroem J S, RIget J. Particle swarm optimization with spatial particle extension [C]//Proceedings of the IEEE Congress on Evolutionary Computation. Honolulu, USA: IEEE, 2002: 1474-1479.
24. Shi Y, Eberhart R C. A modified swarm optimizer [C]//IEEE International Conference of Evolutionary Computation. Anchorage, Alaska: IEEE, 1998: 69-73.
25. FU Qiang. Design and research of a digital comprehensive test system for electric locomotive traction transformer [D]. Changsha, China: Central South University, 2008.