Cooperative Optimization Of Wireless Communication Delay And Channel Efficiency In Smart Grid

As a new generation power grid,smart grid realizes the safe and efficient operation of the power grid.WSNs have the characteristics of low power consumption,scalability,distribution,strong self-organization,etc.,and have widely used in the monitoring and communication fields of smart grids.In the smart grid,the sensor nodes of the WSN are fixed around the device,collect the device status information,and then forward it through the relay node to realize long-distance transmission.Therefore,the end-to-end delay of WSN is directly related to node channel competition,node transmitting power,data transmission rate and so on.Improving the transmitting power of nodes can reduce the forwarding times of relay nodes and reduce the end-to-end delay,but it will also affect more nodes and reduce the channel utilization rate.Meanwhile,the dynamic change of network caused by node failure also has an uncertain influence on the end-to-end delay.According to the above problem,in the static and dynamic networks,in this paper,based on the smart grid wireless communication delay and collaborative optimization efficiency and channel access can balance the end-to-end delay and channel utilization network topology,in effectively reduce the end-to-end delay of WSN nodes at the same time avoid transmission power is too big,to ensure that the wireless communication channel utilization.According to the above problem,in static network and dynamic network,this paper proposes a collaborative optimization based on smart grid wireless communication delay and channel efficiency to balance end-to-end delay and channel utilization,and effectively reduce the end-to-end delay of WSN,at the same time,avoid excessive node transmitting power and ensure the channel utilization of wireless communication.The main work of this paper is as follows:(1)According to the IEEE802.15.4 standard,a Markov chain model is established to obtain the average data processing delay of a single node,and the end-to-end delay of WSN is obtained according to the queue theory.The validity of the model is verified by simulation.(2)In static WSN,with the real-time requirements of smart grid wireless communication as constraints,the particle swarm algorithm is used to synergistically optimize wireless communication delay and channel efficiency.The effectiveness of the algorithm is verified by simulation.(3)In static and dynamic WSN,the wireless communication delay and channel efficiency are collaboratively optimized by a reinforcement learning algorithm based on node grouping.The nodes are grouped according to the Euclidean distance between the nodes,in order to reduce the dimension of the action space and speed up the convergence speed of the algorithm.The effectiveness of the algorithm is verified by simulation.(4)Comparing the adaptability of particle swarm algorithm and reinforcement learning algorithm based on node grouping in two cases of static WSN and dynamic WSN.