# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network  
[PERSON]  
1 College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (R.C.) 1  
[PERSON]  
1 College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (R.C.) 1  
[PERSON]  
2 College of Electronics & Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China 22  
the safety and stability of the power system's operation [4]. During the phasor data transmission process, communication network link failures such as optical fiber and satellite link terminals may occur due to communication network link congestion or severe weather conditions. This prevents the PDC from collecting complete phasor data, rendering the wide-area measurement system incapable of accurately monitoring and controlling the operation of the power system [5]. With the advancement of software defined network (SDN), new solutions have been proposed to address link failure recovery in communication networks [6]. SDN offers a comprehensive network view, allowing for real-time network status information retrieval through the controller. This enables more flexible network control and facilitates rapid identification of faulty links in the network topology [7]. SDN uses standardized interaction protocols between planes to decouple the data plane from the control plane. The data plane swiftly forwards data packets over the failed link based on backup path flow table information in the switch, thereby reducing link failure recovery delay in the network topology [8]. In recent years, the SDN-based wide-area measurement system communication network has attracted the attention of many scholars at home and abroad, which provides a new research idea for the problem of fast recovery of phasor transmission link failure in the wide-area measurement system communication network [9]. This paper proposes the integration of SDN into the wide-area measurement communication network. Furthermore, it puts forth a backup path design method based on graph convolutional neural network (BPDM-GCN) with the objective of enhancing the recovery success rate of faulty links in the communication network topology of wide-area measurement systems of varying sizes. The BPDM-GCN method employs an iterative training process based on the transmission delay, bandwidth occupancy, and packet loss rate of the phasor data link within the communication network of the wide-area measurement system. This process involves multiple interactions between the intelligent agent and the communication network, with the objective of identifying the optimal link weight for calculating the backup path. Based on the optimal link weight in the network topology, with link load balancing and shortest path as the optimization goals. A backup path based on the incremental shortest path tree has been designed and implemented to achieve rapid recovery of faulty links in the communication network of the wide-area measurement system. The contributions of this paper can be summarized as follows:  
(1) Thoroughly assess the performance of phasor data transmission within the wide-area measurement system communication network, investigate the current fault link recovery algorithms, and summarize the issues related to their limited adaptability to various network topologies. (2) A backup path design method based on GCN improved DDPG is proposed. Through multiple interactions between the intelligent agent and the wide-area measurement system communication network, iterative training is performed to output the optimal link weight that can achieve link load balancing. (3) A method for implementing backup paths based on an incremental shortest path tree is designed, with comprehensive optimization objectives of link load balancing and shortest path selection.