A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization

This paper proposes a hybrid topology scale-free Gaussian-dynamic particle swarm (HTSFGDPS) optimization algorithm for real power loss minimization problem of power system. The swarm population is divided into two parts: hybrid topology population and scale-free topology population. The novel hybrid...

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Vydáno v:Engineering applications of artificial intelligence Ročník 32; s. 63 - 75
Hlavní autoři: Wang, Chuan, Liu, Yancheng, Zhao, Youtao, Chen, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2014
Témata:
Algorithms
Convergence
Electric power generation
Gaussian dynamic particle swarm optimization
Mathematical models
Optimization
Power loss
Power system
Real power loss minimization
Scale free network
Swarm intelligence
Topology
Power system
Scale free network
Real power loss minimization
Gaussian dynamic particle swarm optimization
ISSN:0952-1976, 1873-6769
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Shrnutí:This paper proposes a hybrid topology scale-free Gaussian-dynamic particle swarm (HTSFGDPS) optimization algorithm for real power loss minimization problem of power system. The swarm population is divided into two parts: hybrid topology population and scale-free topology population. The novel hybrid topology is mixed with fully connected topology and ring topology. Then, it enables the particles to have stronger exploration ability and fast convergence rate at the same time. In the scale-free part, the topology will be gradually generated as the construction process and the optimization process progress synchronously. As a result, the topology exhibits disassortative mixing property, which can improve the swarm population diversity. This work focuses on a new combination of swarm intelligence optimization theory and complex network theory, as well as its application to electric power system. The presented method is tested on IEEE 14-Bus and 30-Bus power system. The numerical results, compared with other stochastic search algorithms, show that HTSFGDPS could find high-quality solutions with higher convergence speed and probability.
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ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2014.02.018