Title: Data-driven modelling, control, and fault detection of wind turbine systems

Authors: Young-Man Kim

Addresses: Department of CSEP, The University of Michigan-Flint, 303 E. Kearsley St., Flint, MI 48502, USA

Abstract: 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.

Keywords: data driven modelling; FTC; fault tolerant control; wind turbines; recursive identification; fault detection; wind energy; wind power; predictor based system identification; model predictive control; MPC; pitch angle; generator torque; fault tolerance.

DOI: 10.1504/IJSCIP.2014.059687

International Journal of System Control and Information Processing, 2014 Vol.1 No.3, pp.298 - 318

Published online: 06 Mar 2014 *

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