Using Wavelet for Finding Fault Place and Neural Network for Types of Facult in Transmission Lines

Abstract — a transmission line has their own faults and they could be single phase, double phase and three phases to ground. There are different scheme which is related to modern relay that can work with re closer for protecting the faulted phases also there should be accurate selection for finding the right phase. This thesis shows right different scheme for detection and classification of faults on transmission line. The scheme is to use neural network and wavelet transform together, to choose a proper way for solving the problem. Wavelet transform has strong mathematical, very fast and accurate tools for transient signal in the transmission lines beside We use artificial neural network that can make a different between measured signal and associated signal that has different pattern. It can be done by using specific algorithm. This algorithm using time frequency analysis of faulted transient line with help of wavelet transform, and then this will followed to the artificial neural network for identify what phase is faced with the fault. Here we used MATLAB software for simulation of fault signals and verifying the correctness of the algorithm. There will be different types of fault type which is giving to the software and result will show that where and what phase is faced the problem