基于自适应无迹卡尔曼滤波的电力系统动态状态估计

发布时间：2018-04-29 02:29

本文选题：电力系统 + 动态状态估计　；参考：《电网技术》2014年01期

【摘要】：无迹卡尔曼滤波(unscented Kalman filter,UKF)是种非线性滤波方法。由于假设系统噪声的方差为常数,UKF的估计结果会受到未知系统噪声的影响。为减小未知系统噪声对动态状态估计的影响,提出了种改进的自适应UKF(adaptive unscented Kalman filter,AUKF)算法。该方法通过在UKF中引入渐消记忆指数加权的Sage-Husa噪声统计估值器,能够估计时变系统噪声的均值和方差。利用IEEE57和IEEE 118测试系统,在典型日负荷条件下对AUKF方法的有效性进行仿真验证,结果表明所提出的AUKF算法与传统UKF方法相比,在不增加计算复杂度的同时,能够提高状态估计精度。
[Abstract]:Unscented Kalman filter (UKF) is a nonlinear filtering method. The estimation results of the system noise assuming that the variance of the system noise is constant are affected by the unknown system noise. In order to reduce the effect of unknown system noise on dynamic state estimation, an improved adaptive UKF(adaptive unscented Kalman filter algorithm is proposed. This method can estimate the mean and variance of noise in time-varying system by introducing a Sage-Husa noise statistical estimator weighted by fading memory exponent in UKF. Using IEEE57 and IEEE 118 test system, the validity of AUKF method is simulated under typical daily load conditions. The results show that the proposed AUKF algorithm has no computational complexity compared with the traditional UKF method. It can improve the accuracy of state estimation.
【作者单位】：华北电力大学电气与电子工程学院;
【基金】：国家自然科学基金项目(51077053,51277074)~~
【分类号】：TN713;TM711

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