Abstract: Existing optimizations have low precision and slow speed for reconfiguration of distribution network. In order to improve the safety and reliability of distribution network with Distributed Generation (DG), a simplified particle swarm optimization with adaptive inertial weight and full information was proposed based on leap-frog grouping. Firstly, from the viewpoints of reducing the active power loss of the network, increasing the voltage stability, and balancing the load of the feeder, a multi-objective mathematical model for distribution network was established. Secondly, through the Pareto dominance principle, the multi-objective was converted into several single objects with the same dimension, the same attribute and the same order of magnitude according to the standardized satisfaction of fuzzy membership function to make up for the disadvantages subjectivity and disunited dimension of weight method. Finally, in order to avoid random initialization to produce a large number of infeasible solutions, a kind of multi-objective reconfiguration strategy of distribution network with DG-combining Ant Colony Optimization (ACO) algorithm with random spanning tree and improved particle swarm optimization was designed. Through the IEEE33 node distribution system simulation, the experimental results show that the proposed reconfiguration strategy has a decrease of 41.0% in search efficiency compared to Particle Swarm Optimization (PSO) algorithm. Compared to before reconfiguration, the active power loss of the network is decreased by 41.47%, the voltage stability is decreased by 57.0%, and the load of the feeder is improved by 31.25%. The reconfiguration strategy effectively improves the optimizing accuracy and speeds up the optimization, therefore, improves the safety and reliability of distribution network operation.

Key words: reconfiguration of distribution network, standardized satisfaction, simplified particle swarm optimization with adaptive inertial weight and full information, leap-frog thought, Ant Colony Optimization (ACO) algorithm

摘要： 针对有源配电网对安全可靠性的要求较高，而现有的配电网重构算法精度低、速度低的问题，提出了基于蛙跳分组思想的自适应惯性权重的全信息简化粒子群算法。首先，从降低网络有功功率损耗、提高电压稳定性、均衡馈线负荷三个角度考虑，建立配电网多目标数学模型；然后，通过基于Pareto支配原则，采用模糊隶属函数的标准化满意度将多目标转化为相同量纲、同一属性、相同数量级的单目标，弥补加权法带有主观性、量纲不统一的弊端；最后，为保证种群多样性，避免随机初始化产生大量不可行解，结合蚁群优化（ACO）算法随机生成树和改进粒子群算法制定出一种针对含分布式电源（DG）的多目标配电网重构策略。通过对含DG的IEEE33节点配电网系统仿真验证，实验结果表明，与标准粒子群优化（PSO）算法相比，该重构策略寻优效率提高了41.0%，与重构前相比，该重构策略降低配电网有功损耗41.47%，降低电压偏移指数57.0%，改善系统负荷均衡度31.25%。该重构策略有效提高了寻优精度，提高了寻优速度，从而提高了配电网运行的安全可靠性。

关键词: 配电网重构, 标准化满意度, 自适应惯性权重全信息简化粒子群算法, 蛙跳思想, 蚁群优化算法