A Novel Skeleton Network Reconfiguration Method Based on Distributed PSO Algorithm and Hadoop Architecture

Abstract

Main purpose of network reconfiguration stage is to supply power to outage power stations through black start power and to build a stable network, laying foundation for full recovery of loads in the next stage. A network reconfiguration method is proposed to determine optimal target network in network reconfiguration stage. Firstly, the method considers influence of nodes and lines in the network. It means to measure essentiality of power nodes and load nodes by means of a specific importance degree of node, support of branch to node connected with the branch, and branch influence by means of a specific importance degree of branch. Node aggregation degree measuring coverage uniformity of reconstructed network is proposed to reduce failure risk in reconfiguration process and accelerate full recovery of loads in the next stage. Secondly, computational efficiency is lower when single version algorithm is used to solve high dimensional optimization problems, and large scale network reconfiguration is a high dimension optimization problem. Therefore, a distributed particle swarm optimization algorithm based on Hadoop platform is proposed to get optimum solution of the target network taking advantage of cluster computing and storage capacity. Finally, the proposed method is verified on IEEE 30, 57 and 300 systems to demonstrate its efficiency.