# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 3 Faulty-Path Recovery Scheme Based on DDPG-LBBP
### DDPG-LBBP Agent Interacts with the Environment
To ensure the authenticity of the experimental results, a random algorithm was used in the IEEE 30 and IEEE 57 benchmark power system communication networks to simulate the generation of faulty links in the network topology. Multiple experiments were performed on each faulty link recovery method of DDPG-LBBP, (1+2\(\epsilon\))-BPCA, FFRLI, and LIR to statistically analyze the experimental results to ensure the credibility of the experimental results. The experimental results are shown in Figures 7 and 8, where Figure 7 shows the recovery delay of each faulty-link recovery method in the IEEE 30 benchmark test power system communication network, and Figure 8 shows the recovery delay of each faulty-link recovery method in the IEEE 57 benchmark test power system communication network. As shown in Figure 7, in the IEEE 30 benchmark test power system communication network, the fault recovery delay of the DDPG-LBBP faulty-link recovery method proposed in this paper was mainly distributed in the range of 1.9 ms-3.1 ms, only 7.9% of the fault recovery delay exceeded 3.1 ms, and the maximum fault recovery delay was 4.6 ms. The fault recovery delay of the (1+2\(\epsilon\))-BPCA backup path recovery algorithm was mainly distributed in the range of 2.8 ms-4.4 ms, and the maximum fault recovery delay was 5.4 ms. For the FFRLI and LIR faulty-link recovery methods, 22.1% and 68.4% of the faulty-link recovery delays exceeded 4.5 ms, respectively. The LIR faulty-link recovery method aims to reduce backup flow rules as the optimization objective. It does not consider link

Figure 6: IEEE 30 and 57 benchmark power system communication network topologies. (a) IEEE 30 benchmark test. (b) IEEE 57 benchmark test. utilization when designing the backup path, which leads to link congestion during path recovery and results in a higher link recovery delay compared to other algorithms. The FFRLI fault recovery method calculates the backup path according to the importance of the link and recovers the faulty link faster than the LIR fault link recovery method. As shown in Figure 8, in the IEEE 57 benchmark test power system communication network, the fault recovery delay of the DDPG-LBBP faulty-link recovery method proposed in this paper was mainly distributed in the range of 6.9 ms-8.2 ms, only 8.7% of

Figure 8: Faulty-link recovery delay in IEEE 57 communication networks. Figure 7: Faulty-link recovery delay in IEEE 30 communication networks. the fault recovery delay exceeded 8.2 ms, and the maximum fault recovery delay was 9.4 ms. The fault recovery delay of the (1+2\(\varepsilon\))-BPCA backup path recovery algorithm was mainly distributed in the range of 7.4 ms\(\sim\)9 ms, and the maximum fault recovery delay was 10 ms. For the FFRLI and LIR faulty-link recovery methods, 24.2% and 61.3% of the faulty-link recovery delays exceeded 9.4 ms, respectively. The (1+2\(\varepsilon\))-BPCA backup path recovery algorithm uses a backup path approximation algorithm and optimizes the backup path installation process to reduce the faulty-link recovery delay. The DDPG-LBBP algorithm, which is grounded in the enhanced DDPG algorithm and the GRU, demonstrated substantial performance enhancements. Regarding the recovery latency of failed links, our algorithm dynamically fine-tunes the routing strategy in accordance with real-time network states, including link utilization and data transfer latency. Compared with the above three faulty-link recovery algorithms, the DDPG-LBBP algorithm proposed in this paper comprehensively considers the network topology status information to compute disjoint backup paths, resulting in the shortest fault recovery delay. Compared with the better (1+2\(\varepsilon\))-BPCA backup path recovery algorithm, the faulty-link recovery delay was reduced by about 12.26%. Figures 7 and 8 show the corresponding faulty-link recovery delays of the four faulty-link recovery algorithms in the IEEE 30 and IEEE 57 benchmark test power system communication networks.