Title: Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation,
Abstract: 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.