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+2ε)-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+2ε)-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.