Increasing resilience of power systems using intentional islanding; a comparison of Binary genetic algorithm and deep learning based method

Abstract

Several algorithms combining qualitative and quantitative components are currently used for splitting a large interconnected power grid into islands as a measure to provide the best reconfiguration option when a fault appears. The aim of this article is to compare the clustering results of a binary genetic algorithm and a deep learning based method in order to identify the differences and to find in which cases it is rather better applicable each of the techniques.