| As an important part of connecting the power users,accessing the distributed power sources and improving the grid service qualities,the smart distribution power grid plays an important role in improving the power supply reliability and the power quality.With the rapid development of China's distribution power grid,those existing distribution power grids gradually present some limitations,such as the low power supply efficiency,the low reliability,and so on.How to improve the comprehensive service quality of distribution power grid and to build an economical,efficient and practical smart distribution power grid is becoming an important development issue in the power grid.As the “nerve endings” of the sensing layer,wireless sensor networks(WSNs),has been widely researched and developed in smart distribution power grid due to its unique rapid networkingflexibility,scalability,self-organization and self-recovery.Applying WSNs to the online monitoring,the fault location of distribution lines,and to the substation automation can greatly improve the informationization and automation of the distribution power grid.Based on the background of the smart distribution power grid,this his paper studies the routing algorithm of WSNs in intelligent power distribution communication network.Firstly,we analyze some WSNs plane routing algorithms,including the clustering routing algorithm and the opportunistic routing algorithm,and then compare their perforances.According to the structural characteristics of distribution power grid,the overall and local topology design of WSNs in smart distribution power grid is then given.The main problems t to be solved in the design of routing algorithm for smart distribution power grid WSNs are further analyzed.Secondly,considering the large-scale deployment of smart distribution power grid WSNs,the node energy limitation,and the real-time transmission requirements of communication data,motivated by those existing clustering routing algorithms,an improved algorithm for non-uniform clustering routing with energy balance is proposed.In the cluster head election stage,the relative residual energy of nodes and the distance between nodes and the sink nodes are considered as weights to calculate the waiting time of nodes to compete for cluster heads.The smaller the waiting time is,the greater the probability of successful election will be.In the non-uniform clusterstage,the distance between nodes and the sink nodes,together with the distribution density of nodes are comprehensively considered to set the competition radius of nodes.In the data transmission phase,based on the inter-cluster forwarding cost,how to select the optimal multi-hop path between the cluster head and the sink node is studied.Simulation results show that the algorithm can effectively balance the node energy consumption and the extension of the network life cycle.Thirdly,in view of the reliable transmission requirements of intelligent distribution network communication data,considering the poor quality of the node wireless communication link in actual engineering applications,together with the low reliability of data transmission,in order to ensure the reliable data transmission of the proposed non-uniform clustering routing algorithm between clusters,one long-hop minimum spanning-tree based reliability-guaranteed opportunity routing algorithm is proposed.The number of hop-by-hop candidate forwarding nodes is optimized by applying the expected reliability,the distance between sending node and sink node,and the quality of communication link.In the selection and priority determination of candidate forwarding nodes,the link quality,the relative residual energy of nodes,the distance between nodes and sink node are considered comprehensively.Simulation results show that the algorithm can effectively reduce the network energy consumption while the expected reliability is satisfied.Finally,the experimental research on the long-hop reliability guarantee opportunity routing algorithm is carried out.We use the CC2530 node module and the IAR software environment to build the experiment platform,and the feasibility and effectiveness of the algorithm are verified on this experiment platform.Figure [45] table [6] reference [60]... |
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