基于扩展集员滤波的发电机动态状态估计

Dynamic State Estimation for a Power Generator Based on Extended Set Membership Filter

为了提高电力系统机电暂态过程中发电机动态状态估计的精确性,提出了一种考虑转子角功角不等关系的发电机动态模型。在此基础上,进一步提出采用适合非线性滤波问题的扩展集员滤波(Extended Set Membership filter,ESMF)算法对动态模型进行求解。以IEEE 9节点系统为例,对基于ESMF和扩展卡尔曼滤波(Extended Kalman filter,EKF)的状态估计方法进行仿真比较,仿真结果表明,ESMF方法能有效地实时估计电力系统机电暂态过程中发电机功角轨迹,且精度更高。

In order to improve the accuracy of dynamic state estimation for apower generatorduring power system transient process,a dynamic model with considering the unequal relationship between the power angle and the rotor angle is proposed. And on this basis,an algorithm of extended set membership filter（ ESMF）,which is fit to solve nonlinear filtering problem is further put forward to solve the proposed state estimation model. For the example of IEEE 9- bus test system,the performance of ESMF is evaluated and compared with that of extended Kalman filter（ EKF）. The results show that ESMF can estimate effectively the power angle trace of power generator in time with high accuracy.