摘要
分布式电源(DG)的大规模接入电网使得线路潮流方向变得复杂,当线路发生故障时,利用传统粒子群(PSO)算法定位故障区段的准确性不高。针对此问题,提出一种基于天牛须改进粒子群(BAS-PSO)算法的故障定位方法。首先,引入自适应的惯性权重加快算法收敛速度,并利用Tent混沌映射将种群初始化,避免其陷入局部最优解;然后,建立了开关函数和适应度函数,以定位含DG配电网故障;最后,通过Matlab对IEEE 33节点的含DG配电网故障模型进行仿真。结果表明:该算法在含DG配电网故障区段定位中搜索速度和精度更高,并且在信号发生畸变的情况下拥有良好的容错性。
The large-scale connection of distributed generation(DG)to the power grid makes the power flow direction of the line complicated.When the line fails,the accuracy of the fault section location is not high by using the traditional particle swarm optimization(PSO)algorithm.To solve this problem,a fault location method by the particle swarm optimization based on beetle antennae search(BAS-PSO)algorithm is proposed.Firstly,a self-adaptive inertia weight is introduced to accelerate the convergence of the algorithm.The Tent chaotic mapping is used to initialize the population to avoid falling into the local optimal solution.Then,a switching function and a fitness function are established to locate faults in distribution networks with DG.Finally,the fault model of the distribution network with DG of the IEEE 33 node is simulated by Matlab.The results show that the algorithm has higher searching speed and accuracy in fault location of distribution network with DG,and has good fault tolerance in the case of signal distortion.
作者
朱振田
王爱元(指导)
唐鸣
ZHU Zhentian;WANG Aiyuan;TANG Ming(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China)
出处
《上海电机学院学报》
2023年第4期192-197,共6页
Journal of Shanghai Dianji University
