# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
###### Abstract
Firstly, the DDPG-LBBP fault recovery method inputs the state features of the communication network in the WAMS into an improved DDPG algorithm with a Convolutional Neural Network (CNN). The algorithm framework is built with optimization objectives such as link delay and link utilization. During the training process, the gated recurrent neural network is employed to effectively mitigate the problems of gradient vanishing or explosion, thereby improving the training efficiency. After the algorithm converges, the optimal link weights for the network topology are output. Second, the maximum disjoint backup paths are designed to prevent the overlap of nodes and links between the primary and backup paths, thereby avoiding secondary network link failures caused by the presence of overlapping links and nodes in both paths. Finally, we design the maximum disjoint backup paths to prevent the overlap of nodes and links between the primary and backup paths. Additionally, we add a backup tag label to the header of the phase packets in the faulty link, which guides the phase packets through the backup path, enabling the rapid recovery of the faulty link. The contributions of this paper can be summarized as follows:  
* We summarize the transmission characteristics of the wide-area measurement system (WAMS) communication network, analyze the reasons why conventional faulty-link recovery algorithms are unsuitable for WAMSs, and emphasize the importance of minimizing fault recovery time and ensuring network link load balancing during the fault recovery process. * We propose a rapid fault recovery method based on deep reinforcement learning (DDPG-LBBP) and design a suitable algorithmic framework for WAMS networks. During training, the Gated Recurrent Unit (GRU) is employed to mitigate gradient issues and enhance efficiency. After convergence, the model outputs optimal link weights to achieve link load balancing. * We design the maximum disjoint backup path and introduce the backup label mechanism. The former prevents the overlap of nodes and links between the primary path and the backup path, eliminating secondary network link failures. The latter guides the phasor data packets to be transmitted through the backup path, enabling the rapid recovery of the faulty link and enhancing the overall reliability of the WAMS communication network. * The IEEE 30 and IEEE 57 benchmark power system communication networks are adopted as experimental network topologies. Under different data traffic intensities, the DDPG-LBBP algorithm is compared with the (1+2\({}_{\epsilon}\))-BPCA, FFRLI, and LIR methods, respectively. The experimental results show that the DDPG-LBBP fault recovery method has the advantages of low faulty-link recovery delay, low packet loss rate, and high recovery success rate. Compared with the (1+2\({}_{\epsilon}\))-BPCA algorithm, the recovery delay is reduced by about 12.26%, and the faulty-link recovery success rate is increased by about 6.91%. Compared with the FFRLI method, the packet loss rate in the network topology after faulty-link recovery is reduced by about 15.31%.