Deep Reinforcement Learning-Based Approach for Identifying Critical Nodes in Cyber Physical Power Systems

With the continuous development of smart grids, the cyber-physical power system (CPPS) has become the core architecture of modern power systems. However, accurately identifying critical nodes in CPPS to guard against cascading failures remains a severe challenge. Existing methods fail to effectively characterize the hierarchical interactions and cannot capture the dynamic characteristics of cascading failure propagation in real time online, thus resorting to offline evaluation approaches. To address this, this article proposes an online identification method for critical nodes in CPPS using a deep reinforcement learning framework, providing a reference for node protection. This method identifies critical nodes from two different perspectives: network topology and node electrical characteristics. First, corresponding feature representations are designed for different types of nodes. Then, a deep learning framework called CP-DQN, which integrates feature perception and topology perception, is constructed by combining graph attention networks and dueling deep Q-network, enabling adaptive fusion of node topological and electrical features. Simulation results show that the proposed method exhibits superior performance in the IEEE 39 and IEEE 118 bus systems. Compared with several existing mainstream methods, it demonstrates higher superiority and practicality.