基于容积粒子滤波的配电网动态状态估计  

Dynamic state estimation of distribution network based on CPF

作　　者：石倩 刘敏[1] Shi Qian;Liu Min(School of Electrical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025

出　　处：《电测与仪表》2023年第10期87-91,共5页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51967004)。

摘　　要：配电网中分布式电源的渗透率逐渐升高,为确保配电网安全稳定的运行,需要对配电网运行状态进行准确的感知。针对容积卡尔曼滤波(Cubature Kalman Filter,CKF)算法对强非线性非高斯系统滤波精度有限、标准粒子滤波(Particle Filter,PF)选取重要性密度函数不准确的问题,提出了基于容积粒子滤波(Cubature Particle Filter,CPF)的配电网动态状态估计模型,利用CKF算法设计PF的重要性密度函数。既克服了CKF算法要求噪声为高斯分布的限制,又保留了PF算法的强抗干扰能力。仿真结果表明,在高斯噪声和非高斯噪声下,CPF算法比CKF算法滤波精度更高、更灵活。The permeability of distributed generation in distribution network increases gradually.In order to ensure the safe and stable operation of the distribution network,it is necessary to accurately perceive the operation state of distribution network.And Cubature Kalman filter(CKF)has limited filtering accuracy for the strongly nonlinear non-Gaussian system and the importance density function of standard particle filter(PF)is not inaccurate,an Cubature particle filter(CPF)algorithm is proposed to estimate the dynamic state of distribution network.The importance density function of PF is designed through using the cubature Kalman filter(CKF)algorithm.It not only overcomes the restriction of Gaussian distribution noise required in CKF algorithm,but also retains the strong anti-interference ability of PF algorithm.The simulation results show that CPF algorithm is more accurate and flexible than CKF algorithm under Gaussian noise and non-Gaussian noise.

关 键 词：配电网 动态状态估计 PF 重要性密度函数 CPF 

分 类 号：TM711[电气工程—电力系统及自动化]