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。
英文摘要:
Aiming at the situation that there are correlations among measurements of supervisory control and data acquisition (SCADA), this paper proposes a cubature Kalman filter (CKF) method for dynamic state estimation of power system considering SCADA measurement correlation. Firstly, this paper analyzes the reason of measurement correlation for SCADA system. Then, the process noise covariance matrix is derived based on state transition equation, and cubature transformation method is used to calculate the measurement error covariance matrix of SCADA system. The dynamic state estimation process of power system considering measurement correlation is proposed, and the measurement error covariance matrix and process noise covariance matrix are corrected in real time for each estimation. The simulation results on IEEE-39 system demonstrate that the proposed method can significantly improve the accuracy of state estimation results compared with the CKF algorithm without considering measurement correlation.