Dynamic State Estimation of Power Systems with Uncertainties Based on Robust Adaptive Unscented Kalman Filter
Dongchen Hou, Yonghui Sun, Jianxi Wang, Linchuang Zhang, Sen Wang
Abstract
In this study, a robust adaptive unscented Kalman filter (RAUKF) is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model. To address these issues, a robust M-estimator is first utilized to update the measurement noise covariance. Next, to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation, an adaptive update method is produced. The proposed method is integrated with spherical simplex unscented transformation technology, and then a novel derivative-free filter is proposed to track dynamically the states of the power system against uncertainties. Finally, the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system. Compared with other methods, the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.