The three-phase fault detection and classification can be done by using radial basis function neural network with wavelet transform can be proposed in this paper. The Discrete Wavelet Transform (DWT) is used in implementation of wavelet approach and the radial basis function neural network (RBFNN) will be utilized to detect and classify different types of faults. This paper mainly focuses on identifying the faults and classification by obtaining detailed co-officiants of different types of faults. The RBFNN is used to overcome the drawback of wavelet transform for detecting and classification of faults. In this paper the concept of wavelet transforms and RBFNN with power system network is verified with MATLAB Simulink. The simulation results are obtained from wavelet transform technique and radial basis function neural network (RBFNN) for three phase fault detection and classification.
Radial Basis Function Neural Network With Wavelet Transform For Power System Fault Identification And Classification
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