Optimal coordination of over-current relays in microgrids using unsupervised learning techniques

Abstract

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.