High impedance fault location identification using Bayesian analysis in a shipboard power system

Abstract

In a shipboard power system (SPS) there are many possible locations for faults along power lines. It is important to identify the location and isolate these faults in order to protect the equipment and loads. The shipboard systems represented in this research are based on an all-electric ship that is presented by Corzine and a simplified version of the same ship. This research considers faults at the ends on the lines. Sensors collect data in order to determine where the fault has occurred. The fault location identification algorithm being presented uses data collected from simulations of different switch configurations and different loads. After the data is collected, Bayesian techniques are used to determine where the fault is located. An online training technique is presented to adjust to changes in loads over time to increase the accuracy of the algorithm.