Estimating Fault Location in Bipolar HVDC Transmission Lines using Artificial Neural Networks

In the present era of deregulation and competition, demand from every energy supplier is to have good continuity, dependability and reliability. Fault location can play a vital role in achieving this aim. As uninterrupted power supply is the prime demand by all consumers. However, faults in power system will leads to the interruption in power supply and it will make system vulnerable towards system outrage/collapsing and will lead to damage various electrical peripheral of switch gear/ electrical equipment. Hence all faults are required to be detected and clear as soon as possible to restart power supply to consumer. Having accuracy knowledge of fault location will come very handy in reducing system outrage time and they’re by improving continuity and reliability of system. Various researches have been done previously towards finding accurate result. In this paper location detection using various artificial neural network (ANN) techniques is presented. The goal of the work is to prepare a model which can somehow manage to give accurate fault location on HVDC line thus helps in improving the system performance. In this study we are proposing a model formed using ANN model for the purpose of fault location approximation in HVDC transmission line using the information of sending end and receiving end voltage. This proposed work is performed with the help of MATLAB/Simulink environment and simulation using PSCAD software. The results show the superiority in efficiency and precision of present model in fault location detection using ANN than other tools