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A fault classification method by RBF neural network with OLS learning procedure

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4 Author(s)
Whei-Min Lin ; Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan ; Chin-Der Yang ; Jia-Hong Lin ; Ming-Tong Tsay

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

Published in:

IEEE Transactions on Power Delivery  (Volume:16 ,  Issue: 4 )