Fault Recovery Methods for a Converged System Comprised of Power Grids, Transportation Networks and Information Networks

Abstract

Recently, triple-network convergence systems (TNCS) have emerged from the deep integration of the power grid, transportation networks, and information networks. Fault recovery research in the TNCS is important since this system’s complexity and interactivity can expand the fault’s scale and increase the fault’s impact. Currently, fault recovery focuses primarily on single power grids and cyber–physical systems, but there are certain shortcomings, such as ignoring uncertainties, including generator start-up failures and the occurrence of new faults during recovery, energy supply–demand imbalances leading to system security issues, and communication delays caused by network attacks. In this study, we propose a recovery method based on the improved twin-delayed deep deterministic algorithm (TD3), factoring in the shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay, and an improved actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefits in power grid failure scenarios, and demonstrates a strong resilience against communication delays caused by DoS attacks.