基于强跟踪泰勒-卡尔曼滤波器的动态相量估计算法  

Dynamic Phasor Estimator Based on Strong Tracking Taylor-Kalman Filter

作　　者：刘洁波 黄纯[1] 江亚群[1] 汤涛[1] 谢兴[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《电工技术学报》2018年第2期433-441,共9页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金资助项目(51677060)

摘　　要：同步相量估计算法是同步相量测量技术的核心,在电力系统动态条件下如何提高算法的测量精度和改善算法的动态性能至关重要。提出基于强跟踪泰勒-卡尔曼滤波器(STKF)的动态相量估计算法。首先在考虑谐波和噪声影响以及电气信号幅值、相位时变特性的基础上,基于动态相量的泰勒级数展开项建立动态电气信号的状态空间模型;然后考虑到基于泰勒-卡尔曼滤波器(TKF)的相量估计算法在递推估计各状态变量时无法快速跟踪系统参数突变的缺陷,引入强跟踪滤波器的思想,根据理论残差和实际残差的失配程度及时自适应性地调整估计协方差矩阵,增强了算法对时变电气信号的跟踪能力。分析含噪声的数值信号及Matlab/Simulink的仿真故障电压信号,结果表明,STKF算法比泰勒-卡尔曼滤波器(TKF)算法具有更好的动态响应性能和更高的测量精度,且稳定性更好。The synchronized phasor estimation algorithm is the core of the synchronized phasor measurement technology. So it is very important to improve the measurement accuracy and the dynamic performance of the algorithm in the power system dynamic condition. A dynamic phasor estimation algorithm is proposed in this paper based on strong tracking Taylor-Kalman filter（STKF）. First, taken into account the impacts of harmonics and measurement as well as the time-varying characteristics of amplitude or phase, a state space model of dynamic electrical signals is established. Since the TaylorKalman filter（TKF） fails to fast track the system parameters mutation when estimating the state variables, the idea of strong tracking filter was introduced, where the estimation covariance matrix can be adjusted adaptively according to the mismatch degree between the theoretical and the actual residual. This change improved the ability of the traditional Kalman filter to track mutation signal. Test results of both numerical signal with noise and fault voltage signal generated by Matlab/Simulink show that the STKF algorithm has better step response performance, measurement accuracy and stability than the TKF algorithm.

关 键 词：相量测量 卡尔曼滤波器 强跟踪滤波器 动态性能 

分 类 号：TM76[电气工程—电力系统及自动化]