For Transmission Line Fault Type Recognition Based on RBF Neural Network

Abstract:

The study of existing deficiencies, on the basis of overhead transmission line fault detection based on radial basis function (RBF) neural network theory, the fundamental frequency power spectrum as characteristic vector of fault signal, this paper proposes a new method of transmission line fault type identification. The system with complex structure of 10 KV overhead transmission lines as the research object, on the basis of the transmission line model is established by using Simulink software, for different types of short circuit fault simulation sampling, extract fault features, combined with the zero sequence current, as the input vector, establish the RBF neural network for fault type. Results show that: the fundamental frequency in fault signal power spectrum as the feature vector is easy to extract, information is more concise, the RBF neural network in the feasibility in training high, identify accurately and quickly.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 895-899
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.895
• Online since: November 2014
• Authors: Guo Kai Sun, Jing Xiao Yu, Meng Yao Yuan
• Keywords: Fault Identification, RBF Neural Network, Simulation, Transmission Line

References

[1] Sun Guokai. Tian Youwen: Principle of Relay Protection [J]. China Electric Power Press, (2008).

[2] Hou Yuanbin, DuJingyi, Jiangmei: Neural Network [M]. Xi 'an University of Electronic Science and Technology Publishing House, 2007-16-18 Vol125-126.

[3] Badrul H C, Kunyu W. Fault Classification Using Kohen Feature Mapping[J]. International Conference on Intelligent Systems Applications to Power Systems, Jan 28, 1995-Feb 2, 1996: 194-198.

[4] ZhangZhaoning, MaoPeng: For Transmission Line Fault Type Recognition Based on Combination of Neural Network [J]. Journal of Relay, 2001, 29 (4) : 1-5.

[5] Li Dongmin, Liu Zhigang: Based on Wavelet Packet and Artificial Neural Network of Power System Fault Type Recognition. Electric Power Automation Equipment 2009. 1. Vol29. NO. 1.

[6] Liu Fengxia, Liu Qianjin: Fault Type Recognition Based on Fuzzy Neural Network [J]. Journal of Relay, 2006, (3) : 12-14, 12.

[7] Xia Wei, Li Chaohui, Chang Chunteng MATLAB Simulation and Control System for Example Explanation [M]. Beijing: People's Posts and Telecommunications Press, 2008: 145-158.

[8] Dong Chenhui, Peng Xuefeng: MATLAB 2008 Full Guide [M]. Beijing: Electronic Industry Press, (2009).