基于平均群改进鲸鱼优化算法的最优潮流计算  

Optimal power flow calculation with average swarm improved whale optimization algorithm

作　　者：陈将宏 张晨昊 胡炀 CHEN Jiang-hong;ZHANG Chen-hao;HU Yang(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 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：《广西大学学报（自然科学版）》2022年第4期1030-1043,共14页Journal of Guangxi University（Natural Science Edition）

基　　金：国家自然科学基金项目(61771008)。

摘　　要：针对鲸鱼优化算法(WOA)在求解高维、复杂问题时易陷入局部最优的缺点,提出一种平均群改进鲸鱼优化算法(AWOA)求解电力系统的最优潮流问题。采用平均群距离公式更新鲸鱼的种群位置矩阵,避免个体向量选取的随机性,强化算法中包围、搜寻猎物阶段的搜索能力;结合非线性收敛因子,平衡算法的全局搜索和局部开发能力。然后,运用Levy飞行策略在最优解附近进行小范围搜索,帮助算法后期跳出局部最优;在IEEE30节点系统上进行最优潮流的仿真实验,并与现有的其他智能算法进行比较。结果表明:AWOA算法具有更快的收敛速度,更高的求解精度,显著提升了算法的寻优效率,验证了改进方法的有效性。Aiming at the shortcoming of WOA in solving high dimensional and complex problems, an average swarm improved optimization algorithm(AWOA) was proposed to solve the optimal power flow problem in power system. Firstly, the average group distance formula was adopted to update the whale population position matrix, which avoided the random selection of the individual vector and strengthens the searching ability of the algorithm in the two stages of encircling and searching. Subsequently, combined with the nonlinear convergence factor, the global search and local development ability of the algorithm were balanced. In addition, Levy flight strategy was adopted to conduct a small range search near the optimal solution, which was conducive to the later stage of algorithm jumping out of the local optimal. Finally, the optimal power flow was simulated on IEEE 30-bus system and compared with other intelligence algorithms. The results show that AWOA has better performance on convergence speed and solution accuracy, which significantly improves the optimization efficiency of the algorithm and the effectiveness of the improved method has been verified.

关 键 词：鲸鱼优化算法 最优潮流计算 平均群 非线性收敛因子 Levy飞行 

分 类 号：TM744[电气工程—电力系统及自动化]