基于局部分布式学习模型的电力系统WSNs路由算法研究

WSNs Routing Algorithm of Power System based on Local Part Layout Learning Model

利用WSNs监控电力系统的运转状态,逐渐成为智能电网的主要发展趋势之一,针对现有WSNs路由算法覆盖区域小、能耗高的不足,提出基于局部分布式学习模式的路由算法研究。首先针对电力系统WSNs的拓扑结构构建学习模型,并确定节点之间通信的距离与能耗情况,其次选举簇首节点并计算出当前节点与邻居节点数据传输需消耗的能量,最后在保证节点剩余能量充足的同时选出距离最短的路由路径。仿真结果表明,提出算法节点端对端传输效率更高,能量消耗较低,可以最大限度保证了存活节点的数量。

Using WSNs to monitor the operation state of power system has gradually become one of the main development trends of smart grid.In view of the shortcomings of the existing WSNs routing algorithm,such as small coverage area and high energy consumption,a routing algorithm based on local distributed learning mode is proposed.Firstly,a learning model is built for the topology of WSNs in power system,and the communication distance and energy consumption between nodes are determined.Then,the cluster head node is elected and the energy required for data transmission between the current node and neighbor nodes is calculated.Finally,the routing path with the shortest distance is selected while ensuring that the remaining energy of the node is sufficient.Simulation results indicate that the proposed algorithm has higher endto-end transmission efficiency and lower energy consumption,which can guarantee maximum number of surviving nodes.