针对电力系统动态状态估计中数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统量测量间存在相关性的实际情况,文中提出了一种考虑量测相关性的容积卡尔曼滤波动态状态估计方法。进行了SCADA系统量测相关性分析...针对电力系统动态状态估计中数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统量测量间存在相关性的实际情况,文中提出了一种考虑量测相关性的容积卡尔曼滤波动态状态估计方法。进行了SCADA系统量测相关性分析,然后基于状态转移方程推导过程噪声协方差矩阵,基于容积变换方法计算考虑SCADA系统量测相关性的量测误差协方差矩阵,并提出了考虑量测相关性的电力系统动态状态估计流程,每次估计实时修正量测误差协方差矩阵及过程噪声协方差矩阵。IEEE-39节点系统的仿真结果表明,相较于不考虑量测相关性的容积卡尔曼滤波算法,文中方法能够明显提高状态估计结果的精度。展开更多
Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring...Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.展开更多
文摘针对电力系统动态状态估计中数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统量测量间存在相关性的实际情况,文中提出了一种考虑量测相关性的容积卡尔曼滤波动态状态估计方法。进行了SCADA系统量测相关性分析,然后基于状态转移方程推导过程噪声协方差矩阵,基于容积变换方法计算考虑SCADA系统量测相关性的量测误差协方差矩阵,并提出了考虑量测相关性的电力系统动态状态估计流程,每次估计实时修正量测误差协方差矩阵及过程噪声协方差矩阵。IEEE-39节点系统的仿真结果表明,相较于不考虑量测相关性的容积卡尔曼滤波算法,文中方法能够明显提高状态估计结果的精度。
文摘Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.
