Design of a Nonlinear Model-based Predictive Controller for a Wind Turbine Based on PMSG Using an Augmented Extended Kalman Filter

In this paper, a novel nonlinear model-based predictive controller without speed/position sensors is designed for control of a wind turbine permanent magnet synchronous generator (PMSG) and grid. Here, both grid and generator sides are controlled via a predictive mechanism including an optimization subject to nonlinear constraints of current and voltage amplitudes as well as the harmonic distortion magnitude of currents. To have a sensorless design, a Kalman filter is also designed. First, an extended Kalman filter (EKF) is used to estimate the speed and then an augmented extended Kalman filter (AEKF) is designed to estimate the flux without the need to add complex equations. The simulation results show an acceptable performance of the proposed method despite the changes in the reference speed and disturbance.