OPGW State Evaluation Method Based on MSIF and QPSO-DQN in Icing Scenarios

Abstract

A new OPGW state evaluation method based on Multi-Source Information Fusion (MSIF) and Quantum Particle Swarm Optimization & Deep Q-learning (QPSO-DQN) is proposed. Firstly, using MSIF to integrate and unify historical data and real-time monitoring data of OPGW, more comprehensive and accurate OPGW status information was obtained. Then, utilizing the advantages of deep reinforcement learning (DRL) algorithm DQN in handling highly nonlinear problems, various influencing factors related to the operation of OPGW were addressed. Finally, DQN was improved by introducing the QPSO optimization algorithm, which transformed the Q-value function solving in DQN into a function fitting problem and used QPSO as an intelligent agent to fit the function, achieving accurate evaluation of the OPGW operating status. The simulation experiment results show that the proposed method has the highest accuracy in ice weight detection, temperature detection, frequency detection, and optical power detection on the same dataset, reaching 98.85%, 98.97%, 98.13%, and 98.97%, respectively.