Fault Location Estimator Design

Fault location in distribution system is critical issue to increase the availability of power supply by reducing the time of interruption for maintenance in electric utility companies. In this thesis fault location estimator for power distribution system using artificial neural network is developed for line to ground, line to line, line to line to ground and three phase to ground faults in distribution system. To develop this estimator one of rural radial power distribution feeder in Ethiopia, Oromia, Assela substation Gumguma line feeder is used as a test feeder. This feeder is simulated using ETAP software to generate data for different fault condition, with different fault resistance and loading conditions, which is the fault phase voltage and current. It is found that artificial neural networks are one of the alternate options in fault estimator design for distribution system where sufficient distribution network data are available with narrow fault location distance range from the substation. This has benefits in assisting for maintenance plan, saving efforts in fault location finding and economical benefits by reducing interruption time.