基于粒子群克隆遗传算法的配电网络重构研究

【作者】 覃业梅；

【导师】 王击；

【作者基本信息】 中南大学 ， 控制科学与工程， 2008， 硕士

【摘要】 配电网络重构作为配电系统优化运行的重要手段,对提高系统的安全性、经济性和可靠性具有十分重大的意义。本文针对克隆遗传算法(CGA)突变概率难选择和后期收敛缓慢等问题,结合粒子群算法(PSO),提出了粒子群克隆遗传算法(PCGA),构建了PSO变异算子,弥补了粒子群“早熟收敛”及克隆遗传算法的盲目性与无向性,能够根据历史个体记录和群体记录确定进化方向与幅度,同时保证全局收敛;针对配电网重构的特点,对PCGA中的初始种群、编码策略、收敛条件等方面进行优化,并分析了不可行解产生的原因,重点针对双环网和三环网提出了排除不可行解的方法。另外,本文还采用适应于配电网络的列表法数据结构,实现了基于排序法的按层遍历的拓扑识别和基于前推回代法的配电网络潮流计算,并运用MATLAB的GUI工具箱设计配电网重构的操作界面。利用IEEE标准算例,分别以降低网络损耗和以提高供电可靠性为目标,对正常运行和故障运行的配电网络进行重构。仿真结果表明,PCGA应用于配电网络重构能够有效地克服CGA运行速度慢、PSO早熟收敛的缺陷,排除不可行解,有效地改善收敛性能,与其他方法相比具有较高的搜索效率和较快的运行速度。更多还原

【Abstract】 Distribution network reconfiguration is an important method of optimizing the distribution system, which is significant to enhance the security, the efficiency and the reliability of the system.To resolve mutation probability and slow later-period convergence in the clonal genetic algorithm (CGA), this paper combines the particle swarm algorithm (PSO) with it, and proposes the particle clonal genetic algorithm (PCGA). It builds PSO mutation operator, and makes up premature convergence of PSO and blindness of CGA. It ensures evolution direction and range based on historical records and swarm records. Global optimal solution is found with fewer generations and shorter time. According to distribution network characteristic, initial swarm, code strategy and conditions of convergence of PCGA are optimized. The cause of infeasible solutions of distribution network reconfiguration is analyzed, and the method to eliminate infeasible solutions of the both loop and trip loop is given.Besides, this paper adopts list data structure of adapting distribution network, accomplishes topology identification based on layer of traversal sort and backward/forward flow algorithm. Distribution network reconfiguration operation interface is design using GUI of MATLAB. Using IEEE standard examples, this paper separately takes the minimum network loss and the maximum power reliability as the objective functions, and reconfigures the normal and interrupted distribution network. The simulation results indicate that PCGA with application to distribution network reconfiguration can not only avoid slow running rate of CGA and precocity of PSO, eliminate infeasible solutions during search process, and improve convergence capability, but also have higher search efficiency and calculation rate than other methods.更多还原