# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
###### Abstract  
Aiming to address the high recovery delay and link congestion issues in the communication network of Wide-Area Measurement Systems (WAMSs), this paper introduces Software-Defined Networking (SDN) and proposes a deep reinforcement learning-based faulty-link fast recovery method (DDPG-LBBP). The DDPG-LBBP method takes delay and link utilization as the optimization objectives and uses gated recurrent neural network to accelerate algorithm convergence and output the optimal link weights for load balancing. By designing maximally disjoint backup paths, the method ensures the independence of the primary and backup paths, effectively preventing secondary failures caused by path overlap. The experiment compares the (1+2\({}_{\epsilon}\))-BPCA, FFRLI, and LIR methods using IEEE 30 and IEEE 57 benchmark power system communication network topologies. Experimental results show that DDPG-LBBP outperforms the others in faulty-link recovery delay, packet loss rate, and recovery success rate. Specifically, compared to the superior algorithm (1+2\({}_{\epsilon}\))-BPCA, recovery delay is decreased by about 12.26% and recovery success rate is improved by about 6.91%. Additionally, packet loss rate is decreased by about 15.31% compared to the superior FFRLI method. wide-area measurement system communication network; software-defined network; deep reinforcement learning; gated recurrent neural network; backup path 2025  
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terminals prevent the PDC from fully collecting the phasor data. As a result, the WAMS is unable to accurately monitor and control the operation of the power system [4]. Therefore, it is urgent to address the issue of rapid recovery from phase transmission link failures in the communication network of the WAMS to ensure the real-time transmission of phasor data within the system. In recent years, with the rapid development of SDN technology [5], the WAMS communication network based on SDN has attracted significant attention from scholars both domestically and internationally, providing new research perspectives for addressing the rapid recovery of phase transmission link failures in WAMS communication networks [6]. Leveraging SDN's unique three-layer architecture, which separates the data plane, control plane, and application plane, SDN demonstrates distinct advantages in the rapid recovery of faulty links. On the one hand, controllers in the control plane can monitor the operational status of the network topology in real time, enabling the rapid detection and localization of faulty links within the network topology. On the other hand, the centralized configuration and control of the entire network topology enabled by the OpenFlow protocol used in SDN simplifies the distribution and installation of flow rules for primary and backup paths within the network topology [7]. Currently, fault recovery methods can be divided into two categories: control plane-based and data plane-based fast faulty-link recovery methods, depending on whether the participation of controllers in the control plane is required during the faulty-link recovery process [8]. 1. Control Plane-Based Approach for Fast Recovery of Faulty Links  
To address the issue of prolonged recovery delay in control plane-based faulty-link recovery methods, the data plane-based approach for fast recovery of faulty links pre-computes the primary and backup paths using the SDN controller. When a link failure occurs, the switch forwards phase data packets along the backup path without the need for interaction with the controller, thus reducing the recovery delay. In a related study, [PERSON] et al. used the BFD protocol to detect link failures; when a link failure occurred, the FRT method was used to compute the shortest recovery path, thereby reducing the number of flow entries required for the recovery path [9]. [PERSON] et al.