采用局部学习与反向学习机制的人工鱼群算法在含DG配电网重构中的应用  

Application of artificial fish swarm algorithm with the mechanism of local learning and reverse learning in the reconfiguration of distribution network containing DG

作　　者：许喆 刘秀杰 宋健 潘金生 翟爽 公茂法 Xu Zhe;Liu Xiujie;Song Jian;Pan Jinsheng;Zhai Shuang;Gong Maofa(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,Shandong,China;Dongying Power Supply Company,State Grid Shandong Electric Power Company,Dongying 257091,Shandong,China;Dongying Fangda Electric Power Design and Planning Co.,Ltd.,Dongying 257091,Shandong,China)

机构地区：[1]山东科技大学电气与自动化工程学院,山东青岛266590 [2]国网山东省电力公司东营供电公司,山东东营257091 [3]东营方大电力设计规划有限公司,山东东营257091

出　　处：《电测与仪表》2018年第12期60-65,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61703242)

摘　　要：配电网重构是配电网络结构优化的有效手段。考虑到系统运行的经济性,建立了以网络损耗最小为目标函数的数学模型,采用一种具有高效并行优化能力的人工鱼群算法来求解含DG的配电网重构问题。为了克服二进制编码所带来的"维数灾"问题,提出了三大网络简化原则以提高算法的计算效率。当算法陷入"早熟收敛"的怪圈时,引入局部学习与反向学习机制,一部分鱼群根据处于最优位置鱼群之间的差分结果进行动态调节,协同最优种群强化局部搜索;另一部分鱼群则沿最差位置方向进行反向学习,及时逃离局部最优区域,有效地改善了种群的多样性。为了进一步加快算法的寻优效率,在视野与步长参数方面做出了相应的自适应调整。通过算例分析,验证了文中算法的准确性与有效性。The reconfiguration of distribution network is an effective means to optimize the structure of distribution network. Considering the economy of the system,a mathematical model with minimizing the network loss as objective function is established. An artificial fish swarm algorithm with efficient parallel optimization is adopted to solve the distribution network reconfiguration problem with DG. In order to overcome the problem ＂ dimensionality disaster ＂ caused by binary coding,three major network simplification principles are proposed to improve the computational efficiency of the algorithm. When the algorithm falls into the cycle of ＂ premature convergence＂,the mechanism of local learning and reverse learning are introduced. Part of fish swarm adjust the direction dynamically according to the differential results of the fish in optimal position,and work in coordination with optimal population to strengthen the local search; another part of the fish swarm along the worst position start to reverse learning,flee the local optimal area in time and effectively improve the diversity of population. In order to further accelerate the optimization efficiency of the algorithm,the adaptive adjustment is made in the vision and step size parameters. The accuracy and effectiveness of the proposed algorithm are verified by example analysis.

关 键 词：DG 配电网重构 局部学习 反向学习 人工鱼群算法 拓扑简化 

分 类 号：TM93[电气工程—电力电子与电力传动]