A Gene Importance based Evolutionary Algorithm (GIEA) for identifying critical nodes in Cyber–Physical Power Systems

Protecting the critical nodes of a Cyber–Physical Power System (CPPS) is an effective strategy for mitigating the risk of incurring large-scale blackouts. A Gene Importance based Evolutionary Algorithm (GIEA) is proposed to identify a set of critical k nodes by maximizing the total load loss received by end-users. GIEA adopts an importance-based evolutionary strategy to improve the algorithm’s convergence and accuracy, in which the initial node importance metrics are assessed based on dynamic power flows and topology information. Both performance contribution (PC) and coupling failure impact (CFI) are considered in our importance evaluation framework. The impacts of different types of communication nodes on power networks are integrated into the proposed cascading failure model and CFI assessment. Based on the coupling and interdependence information, the strong coupling node pairs are identified to reduce the dimension of the decision vector to improve the computational efficiency of GIEA. The effectiveness and superiority of the proposed methods are illustrated through an example of a coupling CPPS consisting of the IEEE 30-bus model and a communication network with the small-world structure. •Cascading failure model for characterizing interdependence of Cyber–Physical Power Systems (CPPS).•Proposed an importance evaluation method using CPPS power flows and topology information.•Defined connectivity coefficient to quantify the connectivity between a node and the network.•Developed a Gene Importance based Evolutionary Algorithm for identifying critical k nodes.•Dimension reduction for the decision vector based on the coupling strength of node pairs.