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作 者:徐光辉[1,2] 肖克 柯善焜 王淑青 汪繁荣[1,2] XU Guanghui;XIAO Ke;KE Shankun;WANG Shuqing;WANG Fanrong(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan 430068,China;Industrial Institute,Hubei University of Technology,Xiangyang 441000,China)
机构地区:[1]湖北工业大学电气与电子工程学院,武汉430068 [2]湖北工业大学产业研究院,襄阳441000
出 处:《中南民族大学学报(自然科学版)》2023年第4期535-540,共6页Journal of South-Central University for Nationalities:Natural Science Edition
基 金:国家自然科学基金资助项目(61603127)。
摘 要:相量测量单元(PMU)是一种测量电压和电流的设备,随着电力系统的规模和结构越来越复杂,PMU的数量也大幅增加.利用改进蝠鲼觅食优化算法(IMRFO)对PMU进行了配置,使电力系统在正常运行、考虑关键节点和考虑重要线路时都能完全可观测,同时使PMU配置数最少.在蝠鲼觅食算法的基础上增加了混沌映射以提升算法的收敛速度,增加了跳跃因子以提高算法的勘测能力,从而避免算法陷入局部最优.通过度、特征向量和接近度三种指标,利用TOPSIS综合评价寻找系统的关键节点,利用线的中间中心度判断系统的重要线路.对IEEE-14和IEEE-69节点系统进行了实验仿真,结果表明:IMFRO算法与未改进时相对比,在配置时所需的PMU数和迭代次数均有一定的减少,验证了所提方法对解决PMU优化配置问题的有效性和优越性.Phasor measuring unit(PMU)is a device for measuring voltage and current.As the scale and structure of power system become more complex,the number of PMUs increases greatly.The improved manta ray foraging optimization algorithm(IMRFO)is used to configure PMUs,so that the power system can be fully observable during normal operation,considering critical nodes and important lines,and at the same time,the number of configured PMUs is minimized.On the basis of the manta ray foraging algorithm,chaotic mapping is added to improve the convergence speed of the algorithm,and a jump factor is added to improve the exploration ability of the algorithm,so as to avoid the algorithm falling into local optimal.The key nodes of the system are found by TOPSIS comprehensive evaluation through three indexes of degree,feature vector and proximity.The central degree of the line is used to judge the important line of the system.Experimental simulations of the IEEE-14 and IEEE-69 node systems are performed.The results show that compared with IMFRO algorithm without improvement,the number of PMU and iteration times are reduced to a certain extent,which verifies the effectiveness and superiority of the proposed method to solve the problem of optimal PMU configuration.
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