Abstract
Abstract. This paper presents a model to incorporate the secondary effects of wake steering in large arrays of turbines. Previous models have focused on the aerodynamic interaction of wake steering between two turbines. The model proposed in this paper builds on these models to include yaw-induced wake recovery and secondary steering seen in large arrays of turbines when wake steering is performed. Turbines operating in yaw-misaligned conditions generate counter-rotating vortices that entrain momentum and contribute to the deformation and deflection of the wake at downstream turbines. Rows of turbines can compound the effects of wake steering that benefit turbines far downstream. This model quantifies these effects and demonstrates that wake steering has greater potential to increase the performance of a wind farm due to these counter-rotating vortices especially for large rows of turbines. This is validated using numerous large-eddy simulations for three-turbine, five-turbine, and wind farm scenarios.
Highlights
Wake steering is a type of wind farm control in which wind turbines in a wind farm operate with an intentional yaw misalignment to mitigate the effects of its wake on downstream turbines in order to increase overall combined wind farm energy production (Wagenaar et al, 2012)
It is important to note that for Turbine 3 and the total gains, the Gaussian model forecasts a change in power which is symmetrical about changes in Turbine 2, whereas secondary steering (SS) and the Gauss–curl hybrid (GCH) model predict that a −10◦ yaw on a turbine, which is behind a turbine yawed +20◦, is counter-productive, while a complementary +10◦ yaw is more valuable than either Gauss or yaw-added recovery (YAR) would predict
This paper introduces an analytical model that better captures the secondary effects of wake steering in a large wind farm
Summary
Wake steering is a type of wind farm control in which wind turbines in a wind farm operate with an intentional yaw misalignment to mitigate the effects of its wake on downstream turbines in order to increase overall combined wind farm energy production (Wagenaar et al, 2012). Several recent papers proposed a new wake deficit and wake deflection model based on Gaussian self-similarity (Bastankhah and Porté-Agel, 2014, 2016; Niayifar and PortéAgel, 2015; Abkar and Port-Agel, 2015) This model includes added turbulence due to the turbine operation that influences wake recovery (Crespo et al, 1999). Fleming et al (2018a) investigates the importance of considering explicitly the counter-rotating vortices generated in wake steering (Medici and Alfredsson, 2006; Howland et al, 2016; Vollmer et al, 2016) to fully describe wake steering in engineering models These vortices deflect and deform the wake at the downstream turbine. In addition to these simulations, the proposed model is validated using the results of a wake steering field campaign at a commercial wind farm (Fleming et al, 2020)
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