Data–driven modelling, control, and fault detection of wind turbine systems. (10th March 2014) - Kim, Young–Man
In this paper, data–driven system modelling, control, and fault detection technique for wind turbine systems are researched. The developed algorithm is to recursively update system parameters using predictor–based system identification (SID) technique. Updated system parameters are used to design subspace predictive controller for wind turbine systems with constraint on pitch angle and generator torque. The usefulness of this application is highlighted through simulation on a benchmark example, 5 MW NREL/Upwind reference turbine. It shows that developed algorithm which streamlines controller design process from identification to fault detection is useful for making wind turbine systems being tolerant to faults.