摘要
为了更好地利用分布式电源(DG),需要调整配电网开关状态优化网络结构。基于此,旨在利用一种智能算法对含DG的配电网进行优化重构。以网损最小为目标函数,建立配电网重构模型,并给出重构需要满足的约束条件;按照DG接入配电网的接口类型将其分为PQ型、PV型、PI型和PQ(V)型四种类型,选择前推回代法对含DG的配电网进行潮流计算;通过分析二进制粒子群算法(BPSO)与量子粒子群算法(QPSO),提出了一种改进的量子粒子群算法—加权的二进制量子粒子群算法(WBQPSO)。以IEEE33节点配电系统为例,采用二进制编码方式,通过仿真结果可以发现WBQPSO通过对粒子的平均最好位置加权处理,改善种群多样性,提高收敛速度,可以得到更好的网络重构的优化结果。
In order to make better use of distributed generation(DG),it is necessary to adjust the states of switches in distribution network to optimize network structure.Based on this,it is intended to use an intelligent algorithm to optimize the reconstruction of distribution network with DGs.The reconstruction model of distribution network is established to minimize network loss as the objective function and the constrained conditions which are used in network reconstruction are showed as well.DGs are divided into PQ type,PV type,PI type and PQ(V)type according to the interface types of DG accessing distribution network.Forward and backward substitution method is applied to calculate the distribution network flow.An improved particle swarm optimization-weighted binary quantum particle swarm optimization(WBQPSO)is proposed by comparing binary particle swarm optimization(BPSO)and quantum particle swarm optimization(QPSO).Taking the IEEE33 node distribution system as an example,the binary coding method is used to simulate distribution network reconfiguration.The example verifies that WBQPSO can improve the population diversity,accelerate the convergence speed by optimizing the average position of the particles and obtain a better network reconstruction result.
作者
潘欢
杨丽
胡钢墩
Pan Huan;Yang Li;Hu Gangdun(School of Physics and Electronic-Electrical Engineering,Ningxia Key Laboratory of Intelligent Sensing for Desert Information,Ningxia University,Yinchuan 750021,China)
出处
《电测与仪表》
北大核心
2018年第18期31-36,49,共7页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61403219
61463043)
关键词
配电网重构
分布式电源
粒子群算法
量子粒子群算法
二进制
distribution network reconfiguration
distributed generation
particle swarm optimization
quantum particle swarm optimization
binary system
