# 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
In the IEEE 30 and IEEE 57 benchmark test power system communication networks, network topology environments with different traffic loads are set up. The ratio of the number of links successfully recovered during the experiment to the total number of faulty links in the topology is defined as the recovery success rate. The faulty-link recovery success rate of the DDPG-LBBP algorithm was experimentally compared with that of the (1+2\(\varepsilon\))-BPCA, FFRLI, and LIR faulty-link recovery algorithms. The experimental results are shown in Figure 10.

As can be seen from Figure 10, the faulty-link recovery success rate of the DDPG-LBBP algorithm in the IEEE 30 and IEEE 57 benchmark test power system communication networks remained at 94.5%, and the faulty-link recovery success rates of the other three faulty-link recovery algorithms showed a significant downward trend as the traffic in

Figure 10: Recovery success rate of different fault recovery algorithms. tensity in the network topology increased. Since the backup path calculated by the LIR fault link recovery method cannot be adjusted in time according to the network topology, this algorithm had the lowest recovery success rate compared to other fault recovery methods. The (1+2\(\varepsilon\))-BPCA backup path recovery algorithm and the FFRLI fault recovery method had a higher faulty-link recovery success rate than the LIR faulty-link recovery method. In terms of faulty-link recovery success rate, the DDPG-LBBP algorithm exhibited a distinct and highly efficient operational mechanism. This algorithm periodically updates non-overlapping backup paths based on the operational state of the network topology. During the installation of backup paths, a backup label mechanism is incorporated to precisely direct phasor data packets to be transmitted along the backup paths. This effectively minimizes data packet transmission errors and losses, significantly enhancing the recovery success rate of fault links. Compared with the better performing (1+2\(\varepsilon\))-BPCA fault recovery algorithm, it improved the success rate of the faulty-link recovery by about 6.91%. ### Ablation Experiments

To deeply analyze the impact mechanisms of key elements in the DDPG-LBBP algorithm on its performance, this study meticulously designed and conducted ablation experiments, focusing on the roles of the GRU, reward regulation, and path design in the algorithm. The experimental environment was consistent with that described in Section 4.1, and the experiments were carried out based on the communication network topology of the IEEE 57 benchmark test power system. To achieve precise analysis, this study set up three groups of comparative experiments. The first group was DDPG-RNN with the GRU removed, aiming to observe the impact of the GRU absence on the algorithm's performance. The second group was DDPG-GRU with a basic reward mechanism, which was used to explore the differences in the algorithm's performance under different reward mechanisms. The third group was the complete DDPG-LBBP algorithm, which served as a control group to provide a benchmark for comparative analysis. Four key indicators, namely, training time, average recovery delay, average packet loss rate, and average recovery success rate, were selected to comprehensively and systematically evaluate the algorithm's performance from multiple dimensions. Each experiment was independently run 2000 times to ensure the reliability and stability of the experimental results. To deeply explore the information behind the experimental data, an Analysis of Variance (ANOVA) was used to process the experimental data and to determine whether the differences in the mean values of the evaluation indicators under different algorithm configurations were significant, thereby clarifying the impacts of key elements on the algorithm's performance. The results of the ablation experiments are shown in Table 2.

As shown in Table 2, in terms of training time, DDPG-RNN had the longest training time, with a mean value of 356.8 s due to the absence of the GRU.