K-means and Fuzzy Relational Eigenvector Centrality-based Clustering Algorithms for Defensive Islanding

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas 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 partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands 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.