基于通感一体化技术的数字换流站网络覆盖增强方法OA

With the construction of new power systems,digital technology has become the core driving force to improve the quality and efficiency of the grid.In the digital converter station(DCS),the use of drones and robots for inspection often encounters challenges such as roadblocks and abnormal conditions,and the traditional DCS network does not effectively integrate the perception function of the equipment.Therefore,this paper proposes an approach based on integrated sensing and communication(ISAC)and multiple input multiple output(MIMO),which aims to integrate communication and sensing functions in DCS to improve system performance.However,the complex converter station environment and signal fusion process bring about problems such as limited communication coverage and general inductance interference,especially in practical applications,networks need to face special communication requirements such as high bandwidth,low delay and high reliability.In order to solve these challenges,a DCS-MIMO-ISAC network model is designed to optimize the synsense-to-noise ratio of the system,and increase the signal strength through multi-antenna technology and beamforming method,so as to expand the network coverage.Firstly,two strategies of independent design and joint design are proposed in this paper,and the advantages of joint design and MIMO technology are verified by simulation.In addition,considering the actual business scenario of the digital converter station,the simulation results combined with practical application requirements such as load scheduling and equipment monitoring,and further verified the effectiveness of the proposed scheme in the business environment.Further,the deep reinforcement learning algorithm is used to optimize the beamforming strategy to improve the SNR and coverage of the network.The simulation results show that compared with random Angle selection,the proposed scheme significantly improves the total SNR of the system,and has strong practical application potential.