Application of Multi-agent Particle Swarm Algorithm in Distribution Network Reconfiguration

SHU Dasong; HUANG Zhixiong; LI Junye; ZOU Xiaoyang

doi:10.1049/cje.2016.10.015

Volume 25 Issue 6

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(6): 1179-1185

SHU Dasong, HUANG Zhixiong, LI Junye, et al., “Application of Multi-agent Particle Swarm Algorithm in Distribution Network Reconfiguration,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1179-1185, 2016, doi: 10.1049/cje.2016.10.015

Citation:

SHU Dasong, HUANG Zhixiong, LI Junye, et al., “Application of Multi-agent Particle Swarm Algorithm in Distribution Network Reconfiguration,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1179-1185, 2016, doi: 10.1049/cje.2016.10.015

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Application of Multi-agent Particle Swarm Algorithm in Distribution Network Reconfiguration

doi: 10.1049/cje.2016.10.015

1.
Central South University, Changsha 410075, China;
2.
Hunan Radio and TV University, Changsha 410075, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61203031).

Received Date: 2014-04-23
Rev Recd Date: 2014-09-09
Publish Date: 2016-11-10

Abstract

Abstract

An improved algorithm based on Multi-agent particle swarm (MAS) is proposed to solve the distribution network reconfiguration problem in this paper. The approach is a combination of the learning, competition and cooperation mechanism of multi-agent technology and the strategies of Particle swarm optimization (PSO) algorithm. Using the Von Neumann topology structure in PSO algorithm, each particle represents an agent; each agent not only competes and cooperates with its neighborhood, but also absorbs the evolutionary mechanism of PSO algorithm, so as to share the information with the agent of global optimal. The rules of particle renovating reduce unfeasible solution in the process of particle renovating, and it is able to converge to global optimal accurately and quickly. Test on the IEEE 16-node, 32-node and 69-node system shows both a rapid convergence and a good robustness of this proposed approach.
- Multi-agent system,
- Particle swarm optimization,
- Von Neumann topological structure,
- Distribution network reconfiguration

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References(16)

References

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