基于容积粒子滤波的配电网动态状态估计

石倩; 刘敏

石倩,刘敏.基于容积粒子滤波的配电网动态状态估计[J].电测与仪表,2023,60(10):87-91.
SHI Qian,LIU Min.Dynamic state estimation of distribution network based on CPF[J].Electrical Measurement & Instrumentation,2023,60(10):87-91.

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

DOI：10.19753/j.issn1001-1390.2023.10.014

中文关键词: 配电网动态状态估计 PF 重要性密度函数 CPF

英文关键词:distribution network, dynamic state estimation, PF, importance density function, CPF

基金项目:国家自然科学基金项目（ 51967004）

作者中文名	作者英文名	单位
石倩	SHI Qian	贵州大学电气工程学院
刘敏	LIU Min	贵州大学电气工程学院

摘要点击次数: 715

中文摘要:

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。

英文摘要:

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.

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