Q-Learning-Based Resilience Assessment of Weakly Coupled Cyber-Physical Power Systems

The capability of cyber-physical power system (CPPS) to recover from cascading failures caused by extreme events and restore prefailure functionality is a critical focus in resilience research. In contrast to the strongly coupled systems studied by most researchers, this article examines weakly coupled CPPS, exploring result-oriented recovery approaches to enhance system resilience. Various repair methods are compared in terms of the resilience of weakly connected CPPS across different coupling modes and probabilities of failover. Utilizing the Q-learning algorithm, an optimized sequence for network restoration is obtained to minimize the negative influence of failures on network functionality while reducing power loss. The proposed method's effectiveness and generalizability have been comprehensively verified through simulation experiments by establishing weakly coupled CPPS for the IEEE 39, IEEE 118, and IEEE 300 networks and their corresponding scale-free networks. Its rationality was verified through two recovery mechanisms: single-node recovery and multinode recovery. By comparing the proposed method with heuristic recovery methods and optimization-based recovery methods, we found that it can significantly accelerate network recovery, and improve network resilience, achieving better resilience centrality. These findings provide valuable insights for decision making in CPPS recovery work.