Decentralized dynamic state estimation of doubly fed induction generator using terminal measurements

In this paper, phasor measurements taken from stator and rotor terminals are used for dynamic state estimation of a doubly fed induction generator (DFIG) assuming a reduced order model (3rd order) with unknown mechanical input torque. The proposed estimator provides the advantage that the converter and associated controller dynamics as well as turbine and drive train need not be modelled. In addition the reduced order model reduces the computational complexity of state estimator significantly in comparison to previously reported DFIG models (15th order). Also this modeling of DFIG provides an additional benefit of minimal knowledge of system parameters i.e. only induction generator parameters are required to be known. Dynamic State Estimation has been performed using Extended Kalman Filter with Unknown Inputs (EKF-UI). Performance of proposed estimator has been demonstrated on a benchmark IEEE network modified by augmenting a wind farm consisting of multiple DFIG.

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