Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation
For power system operators, this provides an improved EKF-based dynamic state estimation method that balances accuracy and computational efficiency, though it is an incremental improvement over existing Kalman filtering approaches.
This paper proposes an adaptive multi-step prediction (AMSP) approach to improve extended Kalman filter (EKF) performance for estimating synchronous machine dynamic states (rotor angle and speed). The method achieves a good trade-off between estimation accuracy and computational time, validated on a two-area four-machine system using Monte-Carlo simulations.
Power system dynamic state estimation is essential to monitoring and controlling power system stability. Kalman filtering approaches are predominant in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor speed). This paper proposes an adaptive multi-step prediction (AMSP) approach to improve the extended Kalman filter s (EKF) performance in estimating the dynamic states of a synchronous machine. The proposed approach consists of three major steps. First, two indexes are defined to quantify the non-linearity levels of the state transition function and measurement function, respectively. Second, based on the non-linearity indexes, a multi prediction factor (Mp) is defined to determine the number of prediction steps. And finally, to mitigate the non-linearity impact on dynamic state estimation (DSE) accuracy, the prediction step repeats a few time based on Mp before performing the correction step. The two-area four-machine system is used to evaluate the effectiveness of the proposed AMSP approach. It is shown through the Monte-Carlo method that a good trade-off between estimation accuracy and computational time can be achieved effectively through the proposed AMSP approach.