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Control of mechanical loads in wind turbines using an integrated aeroelastic model

Abstract:

This paper presents the integration of an artificial neural network (ANN) based control algorithm for minimizing the stress of a wind turbine (WT) operating above-rated wind speeds, and a coupled aeroelastic model derived from structural blade element momentum (BEM) and thin-walled beam (TWB) theory. The coupled BEM/TWB model is used for real-Time determination of stress, strain and displacement in arbitrary positions of rotating blades. The controller selected is a proportionalintegral-derivate (PID) whose tuning parameters are calculated online through an Adaline ANN. The results show significant improvements in the stress reduction on the blades through the verification of a normalized stress factor. The stress reduction with the proposed control algorithm is up to 27%.