Abstract

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

Highlights

  • Several wind farm control strategies have been explored for maximizing the total power production of a wind farm by coordinating the operation of individual turbines [1]

  • The field data are analyzed during below-rated operation for 1-minute average wind speeds between 5.5 and 12.5 m/s, where the relative power gains from wake steering are expected to be similar, based on FLOw Redirection and Induction in Steady State (FLORIS) predictions

  • The yaw offsets and power are analyzed as a function of wind direction by binning the relevant 1-minute data by wind direction using a bin width of 2◦

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Summary

Introduction

Several wind farm control strategies have been explored for maximizing the total power production of a wind farm by coordinating the operation of individual turbines [1]. Through recent computational fluid dynamics (CFD) studies [4], wind tunnel experiments [5, 6], and field experiments [7, 8, 9], wake steering has been shown to improve total power production for arrays of two to six wind turbines. For purposes of controller design and optimization, computationally efficient wake-steering models are needed. One such control-oriented model is the open-source FLOw Redirection and Induction in Steady State (FLORIS) tool, developed by the National Renewable Energy Laboratory (NREL) and Delft University of Technology [4, 10]

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