For Transmission Line Fault Type Recognition Based on RBF Neural Network
Guo Kai Sun, Jing Xiao Yu, Meng Yao Yuan · Applied Mechanics and Materials · 2014
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.