基于改进层次聚类和GL-APSO算法的配电网动态重构

doi:10.11959/j.issn.2096-6652.202243

Abstract

Abstract:

Aiming at the problem of dynamic reconfiguration of distribution network with distributed generation (DG), a dynamic distribution networks reconfiguration scheme considering the time-varying property of DG and distribution network load was proposed.Firstly, according to the comprehensive similarity between different periods based on both load characteristics and optimal network structure, an improved hierarchical clustering method was used to divide the reconstruction interval into segments.On this basis, the genetic learning adaptive particle swarm optimization algorithm was proposed to realize the dynamic reconstruction with minimum network loss.To tackle the shortcomings such as the lack of speed dynamic adjustment strategy and ease to fall into local optimum in basic particle swarm optimization algorithm, a genetic learning scheme based on the optimal position of individual particles was proposed to enhance diversity and improve global search ability.Adaptive inertia weight and acceleration coefficients were introduced to meet the optimization requirements of different periods.Finally, a simulation was carried out through the IEEE 33-bus distribution system as an example to verify the effectiveness and superiority of the proposed method.

Key words: distribution network dynamic reconfiguration, time division, hierarchical clustering, genetic learning adaptive particle swarm optimization algorithm

CLC Number:

TP273

Yun WANG, Meiyun WANG, Jian ZHOU, et al. Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 410-417.

Figures/Tables 7

References 20

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方案	重构时段	断开开关号	开关操作次数/次	总网损/kW·h
方案1	无	33、34、35、36、37	0	1 459.9
方案2	各时段	各时段最优	42	977.6
方案3	0:00~4:00	33、35、37、14、32	12	989.7
	4:00~8:00	33、9、37、14、32
	8:00~12:00	7、9、37、14、17
	12:00~16:00	7、9、37、14、17
	16:00~20:00	6、9、37、14、32
	20:00~24:00	7、9、37、14、32
方案4	0:00~2:00	33、35、37、14、32	12	990.1
	2:00~3:00	33、35、37、14、32
	3:00~8:00	33、9、37、14、32
	8:00~15:00	7、9、37、14、17
	15:00~21:00	6、9、37、14、32
	21:00~24:00	33、9、37、14、32
方案5	0:00~4:00	33、35、37、14、32	12	983.9
	4:00~8:00	33、9、37、14、32
	8:00~10:00	7、9、37、14、17
	10:00~16:00	7、9、37、14、17
	16:00~22:00	6、9、37、14、32
	22:00~24:00	33、9、37、14、32

算法	最优解（网络损耗/kW）	平均迭代次数/次	潮流计算次数/次	寻优率
PSO	982.8	33	1 650	0.47
GL-PSO	982.8	16	2 400	0.81
PS-HS	982.8	25	1 500	0.68
SA-CS	982.8	28	1 680	0.89
GL-APSO	982.8	14	1 400	0.89

Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm

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