用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法

Improved Robust Unscented Kalman Filtering Algorithm for Dynamic State Estimation of Power Systems

针对动态状态估计中传统无迹卡尔曼滤波(UKF)采样方法的不足,对UKF算法进行改进,每次估计实时调节比例修正因子,提高滤波性能。动态状态估计结果精度受量测粗差影响较大,为此提出一种鲁棒无迹卡尔曼滤波(RUKF)算法,引入粗差判据检测粗差,通过增强因子来降低粗差对系统状态估计结果的影响。将RUKF算法运用于电力系统动态状态估计,仿真结果表明,该算法具有良好的估计性能及较强的鲁棒性。

Aiming at the shortcomings of traditional unscented Kalman filter（UKF）sampling method in the dynamic state estimation,the UKF algorithm is improved by adjusting the ratio correction factor in real time to improve the filtering performance.The accuracy of dynamic state estimation is greatly influenced by the gross error.Therefore,a robust unscented Kalman filter（RUKF）algorithm is proposed.The gross error criterion is introduced to detect the gross errors,and the enhancement factor is applied to reduce the influence of gross errors on system state estimation results.RUKF algorithm has been applied to the dynamic state estimation of the power system and the simulation results show that RUKF algorithm has good estimation performance and strong robustness.