Data-Driven Fault Location of Electric Power Distribution Systems With Distributed Generation

This paper is proposing a data-driven approach for fault location of distribution systems with distributed generations (DGs) by utilizing smart meters at low voltage (LV) networks and remote fault indicators (RFIs) at medium voltage (MV) networks. The determined fault location assists system operators with expedited service restoration, thus improving system reliability and resiliency. To quickly locate a fault, an enhanced escalation method is proposed to use outage reports from smart meters for prediction of the outage region. The determined outage region together with overcurrent notifications from RFIs with directional elements is jointly used to pinpoint the faulty line section. To this end, a new analytical model based on mixed integer linear programming (MILP) is proposed and each hypothetical fault location is modeled as decision variables. The result is an algorithm that is capable to support decision-making of single or multiple faulted line section(s) with incorrect and incomplete data from smart meters and RFIs for accurate fault location. In addition, an engineering way is presented to configure “power outage recognition time” of smart meters and logics for outage escalation are proposed in this paper. Simulation results based on a utility feeder validate the proposed methodology for fault location.