Intelligent Fault Identification System for Transmission Lines Using Artificial Neural Network

Abstract

Transmission and distribution lines are vital links between generating units and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults on electric power transmission lines using artificial neural networks. A feed forward neural network is employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. The developed neural network is capable of detecting single line to ground and double line to ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system. Hardware implementation of neural network is done on TMS320C6713. Keywords: Transmission Line, Asymmetric fault detection, Artificial neural network (ANN), Back Propagation algorithm, DSP processor TMS320C6713, Code Composer Studio, MATLAB/Simulink