Fault Detection and Classification in Power Transmission Lines using Back Propagation Neural Networks

Generally, overhead power transmission system is a set of conductors used to transfer power from generation station to consumer side. Since the conductors are left uncovered, they become more vulnerable to faults. These faults lead to discontinuity of supply and result in power losses, which will be negatively impacting the transmission system efficiency. An efficient and reliable power transmission system must be capable enough to detect and correct such faults. The proposed research work has developed an approach for transmission line fault classification and detection using back propagation neural networks (BPNN). A comparative analysis on various algorithms used in back propagation neural networks, by taking performance metrics as MSE, amount of time taken for training and no. of epochs is included. Simulations are performed using the MATLAB/Simulink® platform.