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. For a spacing of 5 rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain improvement of 128 %. 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.

Wake steering optimization under uncertainty

“…Using a polynomial chaos expansion (PCE) approach, which has not been employed in previous OUU studies of wake steering strategies, we show that direction is the most important uncertain input, effectively smearing out the paths of wakes and reducing the expected velocity deficit. We further show that uncertainty generally reduces optimal yaw offsets, in agreement with the results of Rott et al (2018) and Simley et al (2019) obtained using a simple quadrature approach not based on PCE.…”

“…In this study, we examined how uncertainty affects wake steering strategies and what benefits may be associated with designing these strategies in the presence of operational uncertainty using OUU. While previous approaches (Simley et al, 2019;Rott et al, 2018) have used simple quadrature to estimate AEP, we demonstrated the more efficient PCE approach. We show that uncertainty in inflow direction effectively smears out the wake.…”

“…Subsequently, Rott et al (2018) formulated and solved a wake steering OUU problem for a nineturbine plant, assuming uncertainty in the measured inflow direction. More recently, Simley et al (2019) formulated an OUU problem by taking yaw position uncertainty and inflow direction variability into account.…”

Continued Results from a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 2

“…Finally, Simley et al (2019a) proposes new techniques for evaluating the statistical variation of wind direction in terms of the effect on wake propagation direction, and uses this analysis to design new dynamically optimal controllers. This work has been used in the current, second phase of the study.…”

Wake steering optimization under uncertainty

“…Subsequently, Rott et al (2018) formulated and solved a wake steering OUU problem for a nine-turbine plant, assuming uncertainty in the measured inflow direction. More recently, Simley et al (2019) formulated an OUU problem taking yaw position uncertainty and inflow direction variability into account.…”