Abstract. The performance of an open-loop wake-steering controller is investigated
with a new unique set of wind tunnel experiments. A cluster of three scaled
wind turbines, placed on a large turntable, is exposed to a turbulent
inflow and dynamically changing wind directions, resulting in dynamically
varying wake interactions. The changes in wind direction were sourced and
scaled from a field-measured time history and mirrored onto the movement
of the turntable. Exploiting the known, repeatable, and controllable conditions of the wind
tunnel, this study investigates the following effects: fidelity of the
model used for synthesizing the controller, assumption of steady-state vs.
dynamic plant behavior, wind direction uncertainty, the robustness of the
formulation in regard to this uncertainty, and a finite yaw rate. The
results were analyzed for power production of the cluster, fatigue loads,
and yaw actuator duty cycle. The study highlights the importance of using a robust formulation and plant
flow models of appropriate fidelity and the existence of possible margins
for improvement by the use of dynamic controllers.
Wind condition awareness is an important factor to maximize power extraction, reduce fatigue loading and increase the power quality of wind turbines and wind power plants. This paper presents a new method for wind speed estimation based on blade load measurements. Starting from the definition of a cone coefficient, which captures the collective zeroth-harmonic of the out-of-plane blade bending moment, a rotor-effective wind speed estimator is introduced. The proposed observer exhibits a performance similar to the well known torque balance estimator. However, while the latter only measures the average wind speed over the whole rotor disk, the proposed approach can also be applied locally, resulting in estimates of the wind speed in different regions of the rotor disk. In the present work, the proposed method is used to estimate the average wind speed over four rotor quadrants. The top and bottom quadrants are used for estimating the vertical shear profile, while the two lateral ones for detecting the presence of a wake shed by an upstream wind turbine. The resulting wake detector can find applicability in wind farm control, by indicating on which side of the rotor the upstream wake is impinging. The new approach is demonstrated with the help of field test data, as well as simulations performed with high-fidelity aeroservoelastic models
Abstract. In this paper, an analytical wake model with a double-Gaussian velocity
distribution is presented, improving on a similar formulation by Keane et al. (2016). The choice of a double-Gaussian shape function is motivated by the
behavior of the near-wake region that is observed in numerical simulations and
experimental measurements. The method is based on the conservation of
momentum principle, while stream-tube theory is used to determine the wake
expansion at the tube outlet. The model is calibrated and validated using
large eddy simulations replicating scaled wind turbine experiments. Results
show that the tuned double-Gaussian model is superior to a single-Gaussian
formulation in the near-wake region.
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