Fault Location and Isolation in Power Distribution Network Using Markov Decision Process

Abstract

Precise fault location in distribution network is one of the most important applications among intelligent monitoring and outage management tasks used for realization of self-healing networks. The data gathered from various intelligent sensors installed throughout the power system could be utilized for smart approaches to locating faults, helping the system restoration, reducing outage time and improving system reliability. Since the distribution network is radial, with multiple laterals connected to the main feeder, faults at various locations may lead to the same voltages and currents observed at the substation. In other words, using the substation measurements to calculate the fault location, multiple failure states are possible. In this paper, Markov decision process is used as a tool for the determination of the faulted feeder section and its isolation from the grid. The algorithm is based on transition probabilities among states obtained from intelligent sensors and tested on a radial distribution network with 3 sectionalizers.