State estimation for wind turbine systems

Wind Energy Systems is an area of research which is maturing rapidly due to the concern of long term global energy consumption, currently systems and dynamics research is ongoing in modeling and identification primarily for controller design, state estimation, fault detection and diagnosis. This paper presents a method to estimate and identify a wind turbine mathematical model, which includes structural deformation (the growth of wind turbine size, the loads wind turbine…) and aerodynamic effects. Finding a mathematical model that would be a good representation of the wind turbine system and at the same time suitable for controller design is a very difficult task for wind turbine system is strongly nonlinear. To get an estimate and an identification since of the mathematical model, we used a model detection algorithm, namely the autoregressive method and eigensystem realization algorithm. To improve the state prediction, we used optimal filtering by using the Kalman filter.