Wavelet signal energy with RBFNN and GRNN for fault classification in transmission line with series compensator

Protection of transmission lines including series compensators has been a very important topic. In this paper, wavelet signal energy for fault recognition using advantage of combining two types of neural networks. Radial Basis Function Neural Network (RBFNN) and General Regression Neural Network (GRNN) are proposed using fault current and voltage signals of the series compensated transmission line. The proposed algorithm is very simple and accurate to find out and classify fault types. Several fault cases are studied and simulation results are presented to show the effectiveness of such algorithm. The simulations are done in the MATLAB® environment.