Constrained spectral clustering based controlled islanding

Controlled islanding, which splits the whole power system into islands, is an effective way of limiting blackouts during severe disturbances. Calculating islanding solutions in real time is difficult because of the combinatorial explosion of the solution space occurs for large power system. This paper proposes a computationally efficient controlled islanding algorithm that uses constrained spectral clustering. An undirected edge-weighted graph is constructed based on absolute values of active power flow and constraints related to transmission line availability and coherent generator groups are included by modifying the edge weights of the graph and using a subspace projection. Spectral clustering is then applied to the constrained solution subspace to find the islanding solution. To improve the clustering quality, a pre-processing procedure is used to detect and eliminate outliers in the eigenvectors of the graph before clustering. A robust k-medoids algorithm, which is less sensitive to outliers than the traditional k-means algorithm, is then used for clustering. Simulation results show that the proposed algorithm is computationally efficient when solving a controlled islanding problem in real-time.