DocumentCode :
2622783
Title :
Performance comparison of GA and DEA in solving distribution system reconfiguration problem
Author :
Jazebi, S. ; Hosseinian, S.H. ; Pooyan, M. ; Vahidi, B.
Author_Institution :
Electr. Eng. Dept., Amirkabir Univ. of Technol., Tehran
fYear :
2008
fDate :
22-24 May 2008
Firstpage :
185
Lastpage :
190
Abstract :
Distribution network reconfiguration (DNR) is a crucial issue in the operating horizon of distribution systems. In distribution networks tie switches will be changed to obtain an appropriate network configuration with minimum losses. Distribution networks have hundreds of switches and determining the best status of switches is a complicated combinatorial, non-differentiable constrained optimization problem, which can be solved using heuristic optimization algorithms. Many researches have been focused on introducing appropriate search algorithms to this real-time optimization problem. To judge which method is best suited for this particular problem, performance comparison is necessary. In this paper GA and DEA are compared solving reconfiguration problem from convergence rate and computational time point of view.
Keywords :
combinatorial mathematics; convergence; distribution networks; genetic algorithms; search problems; DEA; GA; combinatorial problem; convergence rate; differential evolution algorithm; distribution network tie switches; distribution system reconfiguration problem; genetic algorithm; heuristic optimization; nondifferentiable constrained optimization problem; search algorithm; Constraint optimization; Convergence; Genetic algorithms; Heuristic algorithms; Load flow; Load management; Power system protection; Power system restoration; Switches; Voltage; Distribution network; differential evolution algorithm; genetic algorithm; power losses; reconfiguration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Optimization of Electrical and Electronic Equipment, 2008. OPTIM 2008. 11th International Conference on
Conference_Location :
Brasov
Print_ISBN :
978-1-4244-1544-1
Electronic_ISBN :
978-1-4244-1545-8
Type :
conf
DOI :
10.1109/OPTIM.2008.4602364
Filename :
4602364
Link To Document :
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