摘要
针对电力系统最优潮流计算问题,提出了一种新的基于Q学习和纵横交叉搜索的粒子群算法。改进的算法在粒子群的寻优模式中引入纵横交叉算子进行优化,加强了全局收敛能力。同时,该改进算法引入Q学习的探索模式,使其在已知的解空间内进行发掘,从而更好地平衡探索与利用之间的关系。为解决Q学习算法的维度灾难问题,使用了状态-组合动作链的方法。IEEE57和IEEE118节点系统的仿真结果表明,所提算法可以增强传统粒子群算法的全局收敛性,有效求解大规模的最优潮流问题。
To solve the optimal power flow in power systems,new particle swarm optimization(PSO)algorithm based on Q learning and crisscross search is proposed.The improved algorithm introduces crossover operator into PSO mode to enhance the global convergence ability.At the same time,introducing the exploration mode of Q learning into the improved algorithm makes the algorithm explore in the known solution space,so as to better balance the relationship between exploration and utilization.In order to solve the dimension disaster of Q learning algorithm,the method of state-action chain is used.Simulation results of IEEE57 and IEEE118 node systems show that the proposed algorithm can enhance the global convergence of the traditional PSO algorithm,and effectively solve large-scale optimal power flow problems.
作者
孟安波
王鹏
丁伟锋
陈顺
梁濡铎
张铮
MENG Anbo;WANG Peng;DING Weifeng;CHEN Shun;LIANG Ruduo;ZHANG Zheng(School of Automation,Guangdong University of Technology,Guangzhou 510006,China)
出处
《华电技术》
CAS
2021年第8期74-82,共9页
HUADIAN TECHNOLOGY
基金
国家自然科学基金项目(61876040)。
关键词
最优潮流
改进粒子群
Q学习
纵横交叉算法
群智能优化算法
optimal power flow
improved particle swarm
Q learning
crisscross algorithm
swarm intelligence optimization algorithm
