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

许喆; 刘秀杰; 宋健; 潘金生; 翟爽

许喆,刘秀杰,宋健,潘金生,翟爽.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[J].电测与仪表,2018,55(12):60-65.
Xu Zhe,Liu Xiujie,Song Jian,Pan Jinsheng,Zhai Shuang.Application of artificial fish swarm algorithm with the mechanism of local learning and reverse learning in the reconfiguration of distribution network containing DG[J].Electrical Measurement & Instrumentation,2018,55(12):60-65.

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

DOI：

中文关键词: DG 配电网重构局部学习反向学习人工鱼群算法拓扑简化

英文关键词:DG the reconfiguration of distribution network local learning reverse learning artificial fish swarm algorithm topology simplification

基金项目:国家自然科学基金项目（重点项目）

作者中文名	作者英文名	单位
许喆	Xu Zhe	山东科技大学电气与自动化工程学院
刘秀杰	Liu Xiujie	国网山东省电力公司东营供电公司
宋健	Song Jian	东营方大电力设计规划有限公司
潘金生	Pan Jinsheng	东营方大电力设计规划有限公司
翟爽	Zhai Shuang	国网山东省电力公司东营供电公司

摘要点击次数: 2587

中文摘要:

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，本文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，验证了本文算法的准确性与有效性。

英文摘要:

Abstract: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 according to the differential results of the fish in optimal position dynamically adjusted the direction, and work in coordination with optimal population to strengthen the local search; another part of the fish 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 the parameters of vision and step length. The accuracy and effectiveness of the proposed algorithm are verified by example analysis.

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