Synchronization estimation of power systems based on adaptive interpolation extended Kalman filter

Efficient and accurate power system synchronization estimation is of great significance for its safe control and operation. Aiming at the shortcomings of the extended Kalman filter (EKF) method in grid synchronization estimation, which has the disadvantages of poor robustness and accuracy limited by the degree of system nonlinearity, a power system synchronization estimation based on Levenberg-Marquardt adaptive interpolation extended Kalman filter (LM-AIEKF) method is proposed. First, metrics that can quantify the degree of nonlinearity of the synchronous estimation system function and the measurement function are constructed; second, based on the nonlinearity metrics, the interpolation factors are determined using a finite state machine model, and an adaptive interpolation strategy is proposed to reduce the degree of system nonlinearity. Again, the Levenberg-Marquardt principle is introduced to optimally solve the nonlinear least squares problem. Finally, the proposed method is tested and verified in the WECC-9 nodal system and the actual system of Henan Power Grid. The experimental results show that the proposed method can effectively mitigate the estimation bias caused by the nonlinearity of the synchronization estimation model, and improve the accuracy and robustness of the synchronization estimation method for power systems.