Central Difference Kalman Filter Approach Based Decentralized Dynamic States Estimator for DFIG Wind Turbines in Power Systems

Renewable energy integrated complex power systems suffer from its intermittency and unpredictability. Dynamic States Estimators (DSEs) with high accuracy can provide useful information for controllers design. However, Doubly Fed Induction Generator (DFIG) is a highly nonlinear system, where non-linear system estimation approaches have to be adopted. In this paper, we proposed a novel Central Difference Kalman Filter (CDKF) based Decentralized Dynamic States Estimator for DFIG interconnected complex power systems. CDKF is derived based on Sterling's polynomial interpolation, which generates advanced sigma points for capturing statistical information. Due to the advent of Phasor Measurement Units (PMUs), the decentralized estimation becomes applicable. Successful operation of the proposed DSE is verified through MATLAB simulations on an IEEE standard test system, and a comparison has been given to Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF) and CDKF approaches based DSE. The result shows that the proposed CDKF based DSE achieves the highest accuracy among them.