Abstract
Abstract. The present paper further develops and experimentally validates the previously published idea of estimating the wind inflow at a turbine rotor disk from the machine response. A linear model is formulated that relates one per revolution (1P) harmonics of the in- and out-of-plane blade root bending moments to four wind parameters, representing vertical and horizontal shears and misalignment angles. Improving on this concept, the present work exploits the rotationally symmetric behavior of the rotor in the formulation of the load-wind model. In a nutshell, this means that the effects on the loads of the vertical shear and misalignment are the same as those of the horizontal quantities, simply shifted by π∕2. This results in a simpler identification of the model, which needs a reduced set of observations. The performance of the proposed method is first tested in a simulation environment and then validated with an experimental data set obtained with an aeroelastically scaled turbine model in a boundary layer wind tunnel.
Highlights
The ability to control a system is often intimately linked to the awareness of the surrounding environment
The environment is represented by the wind inflow, which is characterized by speed, direction, shears, veer, turbulence intensity, presence of impinging wakes, etc
The aerodynamic rotor model is based on blade element momentum theory (BEM), augmented by classical tip and root losses, unsteady aerodynamics and dynamic stall models
Summary
The ability to control a system is often intimately linked to the awareness of the surrounding environment. Alternative sensors are represented by lidars, which are, not yet routinely installed on board wind turbines because of cost, availability, reliability, effects due to weather conditions and lifetime issues In this sense, current wind turbines have only a very limited awareness of the environment in which they operate. A data set is required that covers a desired range of the four wind states While this is not a major issue in a simulation environment where one can generate all desired wind conditions, an identification based on field test data might not be easy or even possible. Some wind parameters might not change much at a given site, e.g. upflow angle and horizontal shear This would clearly be a major hurdle, as a model only knows what is in the data used for training it.
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