Title :
A fault classification method by RBF neural network with OLS learning procedure
Author :
Lin, Whei-Min ; Yang, Chin-Der ; Lin, Jia-Hong ; Tsay, Ming-Tong
Author_Institution :
Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan
fDate :
10/1/2001 12:00:00 AM
Abstract :
This paper presents a new approach to identify fault types and phases. A transmission line fault classification method based on a radial basis function (RBF) neural network with orthogonal-least-square (OLS) learning procedure was used to identify various patterns of associated voltages and currents. The RBF neural network was also compared with the back-propagation (BP) neural network in this paper. It is shown that the RBF approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed approach can be used as an effective tool for high speed relaying
Keywords :
fault location; least squares approximations; power system analysis computing; power transmission faults; power transmission lines; power transmission protection; radial basis function networks; relay protection; RBF neural network; back-propagation neural network; fault classification method; high speed relaying; orthogonal-least-square learning; radial basis function neural network; transmission line protection; Artificial neural networks; Fault diagnosis; Neural networks; Power system protection; Power system simulation; Protective relaying; Radial basis function networks; Testing; Vectors; Voltage;
Journal_Title :
Power Delivery, IEEE Transactions on
