DocumentCode :
3018392
Title :
Hybrid Optimization Strategy for Line Distribution Network
Author :
Zhou, Qingjing ; Lu, Jingui
Author_Institution :
CAD Center, Nanjing Univ. of Technol., Nanjing, China
fYear :
2010
fDate :
25-27 June 2010
Firstpage :
3807
Lastpage :
3810
Abstract :
This paper is involved in the optimization of line distribution network. The mathematical optimization model with the investment, power and energy losses, and the selection of the line and line location, is discussed. A hybrid optimization strategy based on the adaptive probabilistic optimization technique of genetic algorithm and population intellectual technology of particle swarm optimization algorithm is proposed to solve the optimization problem. It selects the optimal number as a global optimum at every circulation, which makes its results better than both genetic algorithm and particle swarm optimization algorithm, then improves the overall performance of the hybrid optimization strategy. It combines the two algorithms by the global optimums and guarantees the independence of the two algorithms. With a practical project exemplified, the hybrid optimization strategy shows its methodological feasibility and efficiency, in terms of a shorter search time and an optimum result.
Keywords :
genetic algorithms; investment; particle swarm optimisation; power distribution economics; adaptive probabilistic optimization; genetic algorithm; hybrid optimization; line distribution network; mathematical optimization model; particle swarm optimization algorithm; population intellectual technology; Gallium; Genetic algorithms; Investments; Mathematical model; Optimization; Particle swarm optimization; Planning; distribution network; genetic algorithm; hybrid optimization strategy; particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
Type :
conf
DOI :
10.1109/iCECE.2010.929
Filename :
5631847
Link To Document :
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