This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in...This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In SAPSO the learning factors of PSO coevolve with the particles.SFLA is combined with the SAPSO algorithm to improve its performance.The proposed algorithm is tested on two distribution test networks.The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.展开更多
文摘This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In SAPSO the learning factors of PSO coevolve with the particles.SFLA is combined with the SAPSO algorithm to improve its performance.The proposed algorithm is tested on two distribution test networks.The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.
