Abstract
The identification of important nodes in the power and communication coupling network is beneficial to prevent the occurrence of cascading failures. It is of great significance to establish a robust and resilient coupling network. This paper proposes a new method for evaluating the importance of power information network nodes based on link-based community division and Betweenness Centrality Based on Neighbor Nodes (BCBNN) algorithm to prevent better power grid cascading failures. This method can evaluate the importance of nodes in the network, except the nodes whose node betweenness is zero. First of all, this article uses the link-based dividing method explores the overlapping communities in the power grid; secondly, it calculates the importance of nodes in a single-layer network based on BCBNN algorithm; finally, it proposes a characteristic node importance assessment method considering single-layer network topology, grid overlapping structure and dual-network coupling. In addition, this paper has conducted experiments on the standard IEEE30-node system and improved IEEE30-node system. Compared with the node shrinkage method and Max-Cas algorithm, the experimental results not only identify traditional nodes, but also mines potential nodes that could cause the grid to fail. It means that the proposed algorithm has a better degree of differentiation in terms of importance assessment.
Published Version
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