An Adaptive Dynamic State Estimation of Synchronous Generator Under Unknown Inputs

The phasor measurement units (PMUs), which are widely distributed at key nodes in the power network, provide a large amount of measurement information for dynamic state estimation. An accurate model is the foundation for ensuring dynamic state estimation. However, due to uncertain factors such as cyber-attacks, aging of device components and differences in operating environments, unknown inputs may exist in the model, seriously affecting the estimation accuracy. To upgrade the estimation performance of cubature Kalman filter (CKF) under unknown inputs, an adaptive CKF method is proposed. By utilizing adaptive factors, the error variance matrix of state variables can be adaptively updated to suppress the impact of unknown inputs on state estimation. Finally, simulations are conducted on the IEEE 39-bus test power system. Compared with traditional unscented Kalman filter (UKF) and CKF, the proposed method has better performance in estimation accuracy and algorithm robustness.