Node Recovery Optimization of Cyber–Physical Power Systems Based on an SEIRD Epidemic Model

This study focuses on the node recovery optimization of cyber–physical power systems (CPPSs), and employs a propagation probability weighted method considering the electrical characteristics of the power network to establish the suscepted-exposed-infected-recovered-dead (SEIRD) epidemiological model. The model considers the complex interaction between infection propagation and power supply demand. Unlike traditional methods, the fault recovery strategy for CPPS in this model integrates multiple factors, including network status, degree distribution and centrality of infected nodes, node admittance, and power supply demand. By calculating a composite score for each fault node, this scoring method effectively prioritizes nodes to optimize the stability of the system. To validate the effectiveness of the proposed method, simulations are conducted on the IEEE 118-bus system. The results indicate that using a scoring-based method for node recovery optimization can significantly enhance the reliability and power supply capability of the system.