邻域退火粒子群算法在配电网重构中的应用

为求解多目标非线性整数组合优化的配电网络重构问题,建立了以电压均衡指数和网损为目标的配电网重构数学模型。为了克服粒子群算法容易局部收敛的不足,提出了一种基于正态分布的局优邻域闭锁方法的退火技术的粒子群算法(LA-PSO),改进了扰动机制,设计了自适应退火策略,对邻域内的粒子执行并行化退火操作,从而弥补粒子群算法爬山能力的不足,提高了算法的全局寻优能力。用3个不同规模的算例测试提出的算法并与基本算法的性能进行了比较。结果表明,该算法有效改进了粒子群优化算法的局部收敛问题,与单一算法相比,在收敛特性、全局寻优能力和稳定性等方面都有所提高。

Application of Simulated Annealing Particle Swarm Optimization Algorithm in Reconfiguration of Distribution Networks

An optimization model of distribution network reconfiguration is established with the objective of voltage balancing index and network loss. To overcome the shortage of PSO, a modified particle swarm optimization based on local optimum simulated annealing (LA-PSO) is proposed. The new algorithm proposed a lock of local optimum area based on Gaussian distribution, improved perturbation mechanism and a self-adaptive cooling schedule. The particles in local optimum area were performed with parallel simulated annealing to strengthen the climbing ability and global search ability of PSO. LA-PSO is applied in three testing cases with different node numbers and is compared with basic algorithms. The results show that LA-PSO helps PSO exceed the local optimum area and is superior in convergence characteristic, global search ability and stability.