蚁群与小波粒子群算法结合优化配电网重构

Optimization of power distribution network reconfiguration combining ant colony and wavelet particle swarm optimization algorithms

为了使配电网故障后或接入DG后能够高效、稳定地重构,提出一种结合蚁群算法(ACO)和改进的小波变异粒子群算法(IPSOWM)的优化算法(ACO-IPSOWM)。以配电网有功损耗最小和节点电压偏差最小为目标函数,并将其加权归一处理转化为单目标问题,然后采用二进制编码开关状态、拓扑修正策略检查配电网辐射性情况,采用蚁群算法初步寻优,采用小波变异扩展有效种群空间,避免陷入局部最优,最后就IEEE 33节点系统故障后或系统节点含DG出力后配网重构进行仿真。实验结果表明ACO-IPSOWM算法在选取合适参数后,能够结合ACO和IPSOWM的优点,重构后效果更好。

An ACO-IPSOWM algorithm combining ant colony optimization(ACO)and improved wavelet mutation particle swarm optimization(IPSOWM)is proposed to reconstruct the power distribution network after failure or adding DG efficiently and stably.The minimum active power loss and minimum node voltage deviation of the power distribution network are taken as the objective function,and converted into single-objective problem after weighting and normalization.The binary coded switching states and topological correction strategy are used to check the radiation of the power distribution network.The algorithm is preliminarily optimized by using ant colony optimization algorithm,and the wavelet mutation is used to extend the effective population space to avoid that the algorithm falls into the local optimum.The power distribution network reconfiguration is simulated after the fault of IEEE 33 node system and DG output of system node.The experimental results show that the ACO-IPSOWM algorithm can combine the advantages of ACO and IPSOWM after selecting the appropriate parameters,and the reconstructed performance is better.