电力系统动态状态估计算法研究

为了提高电力系统动态状态估计的估计精度和收敛速度,引入一种解决非线性滤波问题的新型粒子滤波算法——混合卡尔曼粒子滤波器(Mixed Kalman Particle Filter,MKPF)。该算法采用扩展卡尔曼滤波器(EKF)与无迹卡尔曼滤波器(UKF)混合作为建议分布,得到一种更接近真实分布的近似表达式。仿真算例将MKPF与EKF和UKF进行了对比,比较结果证明在电力系统受到扰动之后,MKPF算法能够快速地收敛于真实值,且具有比EKF与UKF更高的估计精度和稳定性,达到了在线准确估计的要求。

Study on Power System Dynamic State Estimation Algorithm

In order to improve the estimated accuracy and convergence rate of power system dynamic state estimation, a new particle filter for nonlinear filtering problems-Mixed Kaman Particle Filter (MKPF) is introduced in this paper. The algorithm, which utilizes the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as its recommended distribution. It can obtain a more closely approximate expression of the true distribution. The three different algorithms are contrasted in the simulation, and the comparative results prove that the MKPF can quickly follow to the real value after the power system is disturbed and get higher estimated accuracy and stability than EKF and UKF methods, which has meet the requirement of online accurate estimation.