基于无迹卡尔曼滤波的配网状态估计

Distribution Network State Estimation Based on Unscented Kalman Filter

动态状态估计是电力系统分析、监视和规划的重要工具。针对传统扩展卡尔曼滤波(extend Kalman filter,EKF)需要计算雅可比矩阵会产生线性化误差的不足。选用可非线性化计算的无迹卡尔曼滤波(unscented Kalman filter,UKF)进行状态估计。以IEEE33节点系统为例进行状态估计,仿真结果表明,无迹卡尔曼滤波算法具有良好的估计性能。

Dynamic state estimation is an important tool for power system analysis,monitoring,and planning.For the traditional extended Kalman filter(EKF),the linearization error is generated when calculating the Jacobian matrix.The unscented Kalman filter(UKF)which can be nonlinearly calculated is used for state estimation.The IEEE33 node system is used as an example for state estimation.The simulation results show that the unscented Kalman filter algorithm has good estimation performance.