文件名称:A-combination-of-genetic-algorithm-and-particle-s
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A combination of genetic algorithm and particle swarm optimization for optimal
DG location and sizing in distribution systems
Distributed generation (DG) sources are becoming more prominent in distribution systems due to the
incremental demands for electrical energy. Locations and capacities of DG sources have profoundly
impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm
(GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution
systems. The objective is to minimize network power losses, better voltage regulation and
improve the voltage stability within the fr a me-work of system operation and security constraints in
radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems
to demonstrate the effectiveness of the proposed methodology.
DG location and sizing in distribution systems
Distributed generation (DG) sources are becoming more prominent in distribution systems due to the
incremental demands for electrical energy. Locations and capacities of DG sources have profoundly
impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm
(GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution
systems. The objective is to minimize network power losses, better voltage regulation and
improve the voltage stability within the fr a me-work of system operation and security constraints in
radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems
to demonstrate the effectiveness of the proposed methodology.
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A combination of genetic algorithm and particle swarm optimization for optimal.pdf