Defensive islanding using self-organizing maps neural networks and hierarchical clustering

Abstract

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The objective is to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two new partitioning methods, hierarchical clustering and clustering using self-organizing maps neural networks, have been proposed to determine the clusters to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.