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针对扩展卡尔曼滤波(extended Kalman filter,EKF)的不足,将不需要对非线性系统函数进行线性化的无迹卡尔曼滤波(unscented Kalman filter,UKF)方法引入电力系统动态状态估计,采用生成Sigma点数量最少的比例最小偏度单形采样策略进行无迹变换。以IEEE 14系统为算例,仿真结果表明引入UKF后,估计结果的精度有所提高,但算法的效率较低,且数值稳定性较差。进一步引入平方根形式的UKF(square root UKF,SRUKF)模型,IEEE 14及IEEE 30测试系统的仿真结果证明:在不需要大量牺牲计算时间的同时,算法的数值稳定性得到了改善。表明SRUKF的引入对动态状态估计方法的改进是有效的。
In order to overcome the shortcomings of extended Kalman filter (EKF), an unscented Kalman filter (UKF) method which does not need to linearize the nonlinear system functions is introduced into the dynamic state estimation of power system. The minimum number of points minimum skewness single-sample sampling strategy for no trace transformation. Taking the IEEE 14 system as an example, the simulation results show that the accuracy of the estimation results is improved after the UKF is introduced, but the efficiency of the algorithm is low and the numerical stability is poor. Furthermore, the UKF (square root UKF, SRUKF) model with square roots is introduced. The simulation results of IEEE 14 and IEEE 30 test systems show that the numerical stability of the algorithm is improved without sacrificing computation time. It is shown that the introduction of SRUKF is effective to improve the dynamic state estimation method.