Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Abstract

Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filter (AICKF-UI). DFIGs adopt different control strategies in normal and fault conditions; thus, the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases. Consequently, the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs, which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter. Furthermore, as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs, a large estimation error may occur or the DSE approach may diverge. To this end, in this paper, a local-truncation-error-guided adaptive interpolation approach is developed. Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can (1) effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient; (2) accurately track the dynamic states and unknown inputs of the DFIG; and (3) deal with various types of system operating conditions such as time-varying wind and different system faults.