基于改进哈里斯鹰优化算法的配电网动态重构

Dynamic Reconfiguration of Distribution Network Based on Improved Harris Hawk Optimization Algorithm

针对风光荷不确定性的配电网重构问题,建立分布式电源和负荷出力模型,以系统运行成本和电压偏移构建多目标函数。提出一种改进粒子群算法融合K-means(improved particle swarm optimization and K-means,IPSO-Kmeans)聚类算法来划分典型日负荷曲线,将改进哈里斯鹰优化(improved Harris hawk optimization,IHHO)算法应用于配电网重构,进行寻优计算。为了改善哈里斯鹰优化(Harris hawk optimization,HHO)算法种群分布不均、无法完整搜索到最优解空间范围、易于陷入局部收敛等问题,引入佳点集生成种群初始化,提高种群搜索空间的均匀性。将麻雀搜索算法中的探索者位置更新公式与哈里斯鹰优化算法探索阶段的位置更新公式结合,以提高算法的全局搜索能力。利用柯西-高斯变异扰动策略跳出局部最优解。最后在IEEE33节点配网系统仿真,结果表明所提方法的有效性。

Aiming at the problem of distribution network reconfiguration with wind-solar-load uncertainty,a distributed generation and load output model was established.A multi-objective function was constructed with system operating cost and voltage offset.Firstly,an improved particle swarm optimization and K-means(IPSO-Kmeans)clustering algorithm was proposed to divide the typical daily load curve.Then,the improved Harris Hawk optimization algorithm(IHHO)was applied to the distribution network reconfiguration for optimization calculation.In order to improve the Harris hawk optimization(HHO)algorithm,the population distribution was uneven,the optimal solution space range cannot be completely searched,and it was easy to fall into local convergence.The good point set was introduced to generate population initialization to improve the uniformity of population search space.The explorer position update formula in the sparrow search algorithm was combined with the position update formula in the HHO algorithm exploration stage.This improved the global search ability of the algorithm.The Cauchy-Gaussian mutation perturbation strategy was used to jump out of the local optimal solution.Finally,the simulation results of IEEE33 node distribution network system show the effectiveness of the proposed method.