基于反向变异海鸥优化算法的最优潮流计算  

Optimal power flow calculation with reverse mutation seagull optimization algorithm

作　　者：陈将宏 李伟亮 王羲沐 CHEN Jianghong;LI Weiiang;WANG Ximu(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang,Hubei 443002,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《燕山大学学报》2024年第5期396-407,共12页Journal of Yanshan University

基　　金：国家自然科学基金资助项目(52107108)。

摘　　要：针对海鸥优化算法的全局搜索能力差、收敛速度慢的缺点,引入反向变异策略对海鸥初始种群进行选择;结合非线性收敛因子和粒子群算法速度优化,平衡算法全局搜索与局部开发能力,提出了一种反向变异海鸥优化算法,并将其引入最优潮流问题求解。以发电成本、有功网损和节点电压偏移为目标函数进行单目标最优潮流计算,以发电成本分别和有功网损、节点电压偏移的加权和作为多目标函数进行多目标最优潮流计算,并与基于其他智能算法的最优潮流计算结果进行对比分析。IEEE 30节点系统及IEEE 118节点系统仿真结果表明,采用该算法求解最优潮流问题具有搜索精度高、收敛速度快、稳健性强等优势。Aiming at the shortcomings of poor global search ability and slow convergence speed of seagull optimization algorithm,a reverse mutation seagulloptimization algorithm(RMSOA)was proposed to solve the optimal power flow problem.Firstly,the reverse mutation strategy was introduced to select the initial population of seagull.Subsequently,combined the nonlinear convergence factor and particle swarm algorithm speed optimization,the global search and local development ability of algorithm were balanced.Then,the generation cost or active power loss or node voltage deviation were taken as objective functions of the single-objective optimal power flow calculation,the generation cost and its weighted sum with active power loss or node voltage deviation were taken as objective functions of the multi-objective optimal power flow calculation.Optimization results of the proposed RMSOA algorithm were compared with those of other intelligence algorithms.Simulation results of IEEE 30 bus test system and IEEE 118 bus test system indicate that RMSOA algorithms has advantages of higher search accuracy,faster convergence speed and stronger robustness in solvinig optimal power flow problem.

关 键 词：海鸥优化算法 飞行速度优化 算法性能评估 反向变异策略 最优潮流 

分 类 号：TM74[电气工程—电力系统及自动化]