摘要
针对传统灰狼算法在含分布式电源配电网故障区段定位时易陷入局部最优、收敛速度慢等缺点,引入粒子群算法和α狼局部搜索策略,并对控制参数a作非线性处理,提出了一种基于粒子群灰狼混合算法的含分布式电源配电网故障区段定位方法。首先,构建含分布式电源的IEEE33节点配电网仿真模型,用于模拟配网中发生的单重与多重故障。其次,将粒子群灰狼混合算法与粒子群算法、灰狼算法进行多方面仿真对比,验证所提算法的有效性。结果表明:粒子群灰狼混合算法在含分布式电源配电网故障区段定位中可准确、快速地定位故障区段,同时对畸变信息具有较好的容错能力。
To address the shortcomings of the traditional Gray Wolf Optimization algorithm in fault zone location of distribution networks containing distributed power supplies,such as easy to fall into local optimum and slow convergence speed,the Particle Swarm Optimization and α wolf local search strategy are introduced,and the control parameter a is treated nonlinearly,and the fault zone location method of distribution networks containing distributed power supplies based on a Particle Swarm Optimization and Grey Wolf Optimization hybrid algorithm is proposed.Firstly,an IEEE 33-node distribution network simulation model with distributed power supplies is constructed for simulating single and multiple faults occurring in the distribution network.Secondly,the Particle Swarm Optimization and Grey Wolf Optimization hybrid algorithm is compared with Particle Swarm Optimization and Grey Wolf Optimization in various simulations to verify the effectiveness of the proposed algorithm.The results show that the Particle Swarm Optimization and Grey Wolf Optimization hybrid algorithm can accurately and quickly locate fault zones in distribution networks containing distributed power supplies,and it also has good fault tolerance for distortion information.
作者
余加民
艾青
YU Jiamin;AI Qing(College of Intelligent Systems Science and Engineering,Hubei Minzu University,Enshi 445000,China)
出处
《现代信息科技》
2023年第17期168-173,177,共7页
Modern Information Technology
关键词
配电网
粒子群算法
分布式电源
灰狼算法
故障区段定位
distribution network
Particle Swarm Optimization
distributed supply
Grey Wolf Optimization
faulty section location
