Robust forecasting-aided state estimation of power system based on extended Kalman filter with adaptive kernel risk-sensitive loss.

State estimation (SE) plays a pivotal role in the development of modern power system. Accurate forecasting-aided state estimation (FASE) can track the sudden changes of power system state and maintain the safe operation of modern power system. However, the performance of existing FASE methods is affected by anomalies in real power system, such as sudden state changes and bad data. To address this problem, a robust algorithm based on extended Kalman filter (EKF) with the kernel risk-sensitive loss (KRSL) (called KRSL-EKF) is proposed for FASE. The KRSL-EKF, taking KRSL as the cost function of the original EKF algorithm, can overcome the limitations of the EKF to perform higher estimation accuracy under non-Gaussian noise cases. In addition, an adaptive method is further introduced into the proposed KRSL-EKF algorithm to adjust the covariance matrices of process noise and measurement noise, and we denote it as AKRSL-EKF. The novel AKRSL-EKF algorithm can effectively adapt the noise to system state variations and achieves better estimation accuracy. The effectiveness of the proposed algorithms for FASE is verified on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems. The results show that the estimation accuracy of the proposed algorithms is 30% higher than other traditional algorithms, with high estimation accuracy. • The KRSL-EKF algorithm improves the estimation accuracy of FASE. • The AKRSL-EKF algorithm can effectively track the change of system state. [ABSTRACT FROM AUTHOR]