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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| Published in: | Engineering applications of artificial intelligence Vol. 32; pp. 63 - 75 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2014
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Chen, Yang Zhao, Youtao Liu, Yancheng Wang, Chuan |
| Author_xml | – sequence: 1 givenname: Chuan surname: Wang fullname: Wang, Chuan email: chuanwang0101@163.com – sequence: 2 givenname: Yancheng surname: Liu fullname: Liu, Yancheng – sequence: 3 givenname: Youtao surname: Zhao fullname: Zhao, Youtao – sequence: 4 givenname: Yang surname: Chen fullname: Chen, Yang |
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| Keywords | Power system Scale free network Real power loss minimization Gaussian dynamic particle swarm optimization |
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| SubjectTerms | 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 |
| Title | A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization |
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