基于混合粒子群算法的配电网故障重构研究

Research on distribution network fault reconfiguration based on hybrid particle swarm optimization

为了实现含分布式电源的配电网络故障时的恢复供电,在分析粒子群算法基本原理与配电网络结构模型的基础上,提出一种基于混沌映射改进的自适应混合粒子群算法。将压缩因子与自适应权重引入粒子群算法,并借鉴遗传算法中的杂交与自然选择思想,在每次迭代中根据杂交率选取一定粒子进行两两杂交,把每次迭代结果中优秀的一半替换差的一半,并对适应度值良好的粒子进行Logistic混沌优化。接入分布式电源的IEEE 33节点算例,模拟不同算法进行故障重构。仿真测试结果体现出了改进算法具有更快的收敛速度与更好的稳定性。

In order to restore power supply in distribution network with distributed power supply,an improved adaptive hybrid particle swarm optimization algorithm based on chaotic mapping is proposed based on the analysis of the basic principle of particle swarm optimization algorithm and distribution network structure model.The compression factor and adaptive weight are introduced into the particle swarm optimization algorithm,and the ideas of hybridization and natural selection in genetic algorithm are used for reference.In each iteration,certain particles are selected according to the hybrid rate for pairwise hybridization.In each iteration,the excellent half of the results are replaced by the poor half,and then the particles with good fitness value are carried out Logistic chaos optimization.An example of IEEE 33 nodes connected to the distributed power supply is given to simulate different algorithms for fault reconstruction.The simulation results show that the improved algorithm has faster convergence speed and good stability.