Design and analysis of a wake steering controller with wind direction variability

Abstract

Abstract. Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

Wind tunnel testing of wake steering with dynamic wind direction changes

“…Taking into account the time scaling factor, this means that wind direction fluctuations characterized by a period above approximatively 2 min are missing from the tunnel flow. Since these are the dominant wind direction changes for wind farm control (Simley et al, 2020), a way is needed to fill the lower band of the spectrum.…”

“…The turntable signal is filtered and provided as wind direction input to the controller. By filtering this signal, the controller reacts only to low-frequency fluctuations and neglects higher-frequency turbulent changes, which is desirable for yaw-based control (Simley et al, 2020;Fleming et al, 2019). However, increasing the filtering action generates longer delays, which has the effect of changing the wind direction seen by the controller with respect to the true one.…”

“…For robustness, wind direction and yaw uncertainties should be taken into account in the calculation of the LUTs (Quick et al, 2017;Rott et al, 2018;Simley et al, 2020). Here only uncertainties in wind direction were considered, because yaw uncertainties due to possible sensor errors are negligible for the calibrated G1s.…”

“…However, steady models lack the flow dynamics at the lower frequencies and the delays caused by the advection downstream with a finite travel speed. These models are therefore only capable of predicting slow changes of wind turbine power (Simley et al, 2020). A robust control formulation (Rott et al, 2018) should take into account the uncertain knowledge of the wind direction at these slower timescales.…”

“…Wind farm control is widely recognized as one of the main solutions to mitigate wake effects (Gebraad et al, 2016;Fleming et al, 2016;Vali et al, 2017;Raach et al, 2018;Fleming et al, 2019). In wind farm control, the turbines in a wind farm operate in a coordinated, collaborative fashion.…”