DocumentCode :
648054
Title :
Specialized genetic algorithm to solve the electrical distribution system expansion planning
Author :
Camargo, V. ; Lavorato, Marina ; Romero, Ruben
Author_Institution :
Dept. of Math. of Sinop, Mato Grosso State Univ., Sinop, Brazil
fYear :
2013
fDate :
21-25 July 2013
Firstpage :
1
Lastpage :
5
Abstract :
A specialized genetic algorithm with a adaptation of Chu-Beasley algorithm is presented in this paper to solve the electrical distribution distribution system expansion planning (DSP) problem modeled by a mixed integer nonlinear programming problem. The specialized genetic algorithm proposed in this paper starting from a initial population where all elements have a radial topology found using a heuristic algorithm and after the selection and mutation operations must also go through a local improvement in order to make the proposed solution in a feasible solution, if necessary, with respect to operational constraints. The DSP problem presented in this paper consider the circuit construction/recondutoring for different types of conductors and the substation construct/reinforcement. To evaluate the quality of the proposed methodology were used three different test systems found in the literature, 23, 54 and 136 buses systems.
Keywords :
genetic algorithms; integer programming; nonlinear programming; power distribution planning; 136 buses system; 23 buses system; 54 buses system; Chu-Beasley algorithm; DSP problem; circuit construction; circuit recondutoring; electrical distribution system expansion planning; heuristic algorithm; mixed integer nonlinear programming problem; radial topology; specialized genetic algorithm; Digital signal processing; Genetic algorithms; Planning; Proposals; Sociology; Statistics; Substations; Distribution network planning; genetic algorithm; mixed integer nonlinear programming; power systems optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
ISSN :
1944-9925
Type :
conf
DOI :
10.1109/PESMG.2013.6672615
Filename :
6672615
Link To Document :
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