Critical Node Identification for the Power Optical Cable Network Based on Improved Local Neighborhood Search

With the rapid development of power grid system, the needs of information transmission soar. This also leads to the topological complexity increasing of power communication network. The power cable network today becomes a complex network. In such context, damage of a quite few critical components can induce high loss to the entire network. Thus, identification of these critical nodes for power communication network is highly important. In this paper, a method based on an improved local neighborhood search (ILNS) is proposed to identify the critical nodes in a power optical cable network. Firstly, a model based on graph theory is designed to describe the topology of a power optical cable network. To assess the damage caused by node disruptions, Size of the largest connected component is introduced. Then, the ILNS-based method is proposed to identify critical nodes, i.e., communication station nodes whose disruption will decrease the performance most greatly. Finally, a case study based on a real power optical cable network is presented to validate the proposed method.