基于容积粒子滤波的配电网状态估计  

State Estimation of Distribution Network Based on CPF

作　　者：石倩 刘敏[1] SHI Qian;LIU Min(The Electrical Engineering College,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵州贵阳550025

出　　处：《电力科学与工程》2020年第3期25-29,共5页Electric Power Science and Engineering

基　　金：国家自然科学基金资助项目(51967004);贵州省科技创新人才团队项目(黔科合平台人才[2018]5615)。

摘　　要：高精度的状态估计是配电网安全稳定运行的基础。粒子滤波(Particle Filter,PF)选取重要性密度函数不准确以及卡尔曼框架下滤波方法对非线性系统滤波精度有限的问题,把容积粒子滤波(Cubature Particle Filter,CPF)引入配电网状态估计中。鉴于容积卡尔曼滤波(Cubature Kalman Filter,CKF)在状态更新阶段融入了最新量测,因此在粒子滤波框架下,利用CKF算法设计PF的重要性密度函数,采样获得的带权值粒子更加逼近真实后验分布,提高了状态估计精度。在三相不平衡配电网中进行仿真分析,结果表明,CPF算法比UKF滤波精度高。Because high precision state estimation is the basis of safe and stable operation of distribution network,the importance density function of the particle filter(PF)is not accurate and the filter accuracy of the Kalman filter method for the nonlinear system is limited,the cubature particle filter(CPF)is introduced into the state estimation of the distribution network.In the framework of particle filter,the importance density function of PF is designed by the Cubature Kalman filter(CKF)which incorporates the latest observations into state updating phase.The approximation to the system posterior density is improved by the weighted particle obtained by sampling so that the accuracy of state estimation is enhanced.The simulation analysis is carried out in three-phase unbalanced distribution network.The results show that the CPF algorithm is more accurate than UKF.

关 键 词：配电网状态估计 PF 重要性密度函数 CPF 

分 类 号：TM721[电气工程—电力系统及自动化]