Fault diagnosis of transmission system based on Wavelet Transform and Neural network

Abstract

One of the most important components of power systems are power transmission lines. Different types of faults in power transmission lines may cause disruption of power transmission or damage power system equipment, as well as it can effect on the power quality of the entire network. Therefore accurate estimation of fault location in power transmission for restoring power transmission at the shortest possible time with the lowest disruption at power transmission is vital. On the other hand accurate estimation of type and location of faults in transmission lines can save time and maintenance cost of power system equipment. In this paper, EMTP software is used to simulate a real power grid model with 100 km transmission line for different fault locations and fault resistances. Then Discrete Wavelet Transform DWT, which is anadvance signal processing tool, is applied to acquire fundamental harmonics of three phase voltage and current signals at the end of transmission line. To classify type of faults and their locations, artificial neural network is utilizedat transmission line. The obtained results show that the error percentage in both location and fault typediagnosis is so low.