基于连锁环网与改进离散粒子群算法的多目标配电网重构

Multi-objective distribution network reconfiguration based on chain loops and improved binary particle swarm optimization

配电网重构本质上是一个复杂的高维数非线性组合优化问题。为避免其不可行解的影响,同时实现快速寻优,提出了一种通过连锁环网矩阵快速判断粒子是否满足配电网拓扑约束的方法。采用基于Pareto准则的离散二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)以求解配电网重构多目标优化问题。从三方面对BPSO算法进行改进:改进粒子更新策略以提升新代粒子的可行概率;改进sigmoid函数同时提出邻域搜索机制以强化算法后期的收敛能力;提出基于次优解保留策略的小生境共享机制以改进群体最优粒子更新方式,进而强化算法的全局搜索能力。对IEEE33系统算例进行仿真,结果表明改进BPSO算法在求解含分布式电源(Distributed Generation,DG)的配电网重构多目标优化问题时,能够更加精确高效地收敛至Pareto最优前沿。

Distribution network reconfiguration is essentially a complex multi-objective nonlinear integer combinatorial optimization problem.In order to overcome the influence of the infeasible solution and achieve fast convergence,a fast method for judging whether particles meet topological constraints through chain loops matrix is proposed.A discrete Binary Particle Swarm Optimization(BPSO)algorithm based on the Pareto criterion is proposed to solve the multi-objective optimization problem of a distribution network.The algorithm is improved from three aspects:improving the method for updating particles to increase the feasible probability of the new particles;improving the sigmoid function and proposing a neighborhood search mechanism to enhance the convergence ability of the algorithm in the later stage;proposing a niche sharing mechanism based on the sub-optimal solution retention strategy to improve the method for updating a group's optimal particle,thereby enhancing the algorithm's global search capability.Through the IEEE33 node distribution system,it is verified that the improved BPSO can converge to the Pareto optimal frontier accurately and efficiently when solving the multi-objective optimization problem of a distribution network with Distributed Generation(DG).