基于粒子群和变邻域差分进化搜索算法的配电网重构

Distribution network reconfiguration based on particle swarm optimization and variable neighborhood differential evolution search algorithm

为提高电力系统的经济运行，配电网重构起着重要的作用。大多数的人工智能算法一定程度上依赖于网络初始结构和参数的设置，其收敛速度比较慢。论文采用二进制粒子群算法（BPSO）来对配电网优化．该算法具有并行处理特点、参数少容易控制、收敛速度快的，但经过多次迭代搜索后容易陷入局部最优。因此将变邻域差分进化搜索与BPSO相结合，既保证了群体的多样性义继承了上一代的优越性。通过两个算例的仿真证明了本文算法的可行性，能够有效地搜索到全局最优解。

To improve power system economic operation, distribution network reconfiguration plays an important role.Most of the artificial intelligence algorithms to some extent dependent on the initial structure and settings of the network parameters, its convergence rate is slower.In this paper, the binary particle swarm optimization （Binary Particle Swarm Optimization, BPSO） is optimized the distribution networks.The algorithm has the characteristic of the parallel processing, less parameters, fast convergenc and easy to control, but after searching through several iterations easily fall into local optimum.Therefore combining BPSO with variable neighborhood search and differential evolution, not only to ensure diversity of the older generation but also to inherit the advantages of the previous generation.The simulations of two examples prove the feasibility of this method whitch can effectively search the global optimal solution.