基于自适应反向学习秃鹰搜索算法的最优潮流计算  

Optimal Power Flow Calculation Based on Adaptive Opposition-based Learning Bald Eagle Search Algorithm

作　　者：陈将宏 胡炀 饶佳黎 李伟亮 CHEN Jianghong;HU Yang;RAO Jiali;LI Weiliang(College of Electrical Engineering and New Energy,China Three Gorges University,Hubei Yichang 443000,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443000

出　　处：《电工材料》2023年第1期85-93,共9页Electrical Engineering Materials

摘　　要：针对秃鹰搜索算法(BES)易陷入局部最优、全局搜索与局部开发难以平衡的缺点,引入反向学习策略,促使秃鹰个体进行竞争,结合柯西变异策略和自适应惯性权重因子,提出了一种自适应反向学习秃鹰搜索算法(AOBES),并将其引入最优潮流问题求解。IEEE30节点系统仿真结果表明,采用AOBES算法求解最优潮流问题具有寻优精度高、稳健性强等优势。Aiming at the shortcomings of the BES algorithm that it is easy to fall into local optimum, global search and local development are difficult to balance, the reverse learning strategy is introduced to prompting bald eagle individual competition, and combined with the Cauchy variation strategy and the adaptive inertia weight factor, an AOBES algorithm is proposed and introduced into the optimal current problem to solve. The simulation results of IEEE30 node system show that AOBES algorithm is used to solve the optimal power flow problem, which has the advantages of high optimization accuracy and strong robustness.

关 键 词：秃鹰搜素算法 柯西变异 自适应惯性权重 反向学习策略 最优潮流 

分 类 号：TM74[电气工程—电力系统及自动化]