Wind tunnel testing of wake steering with dynamic wind direction changes

Abstract

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.

Experimental results of wake steering using fixed angles

“…It provides supporting evidence to the physical models proposed in Martínez-Tossas et al (2019) and King et al (2020). These can be combined with wind tunnel studies such as Zong and Porté-Agel (2020); Campagnolo et al (2020) to gain confidence in these wake-steering models. First, using these models in the design of wind farm controllers produces different control strategies from prior deflection models and can be critical in the design of control strategies for large arrays, because of the secondary steering effects (see, for example, Zong and Porté-Agel (2021)).…”

How realistic are the wakes of scaled wind turbine models?

“…However, in reality the behavior of the blades and, as a consequence, of the wake is much more strongly affected by the chord-based Reynolds number, as initially discussed in Bottasso et al (2014a). In fact, the much lower Reynolds regime of a small-scale model blade compared to a full-scale machine implies very different aerodynamic characteristics of the airfoils, which in turn drive a number of specific design choices of the scaled model (Bottasso and Campagnolo, 2021;Canet et al, 2021). Notwithstanding the differences caused by the chord-based Reynolds number mismatch, it is relatively easy -as shown more in detail later on -to match the main processes taking place in the outer shell of the near wake, as well as the ones that govern its breakdown and the characteristics of the far wake.…”

“…-Detailed flow measurements are possible with a plethora of devices, from standard pressure and hot-wire probes to particle image velocimetry (PIV) (Meinhart, 1999) and scanning lidars (van Dooren et al, 2017), whereas measurements of comparable accuracy and resolution are today hardly possible at full scale. Additionally, time flows faster in a scaled experiment than at full scale (Bottasso and Campagnolo, 2021;Canet et al, 2021;Campagnolo et al, 2020), which means that a large informational content can be accumulated over relatively short periods of time.…”

“…Following Bottasso and Campagnolo (2021) and Canet et al (2021), dimensional analysis and wake physics are used here to review the main factors driving wake behavior. The same analysis also reveals which physical aspects of full-scale wakes cannot be matched at the reduced scale and with the considered experimental setup.…”

Identification of airfoil polars from uncertain experimental measurements

“…FVW methods (Sebastian and Lackner, 2012;Shaler et al, 2019) are not yet routinely used because of their higher computational costs but offer promising alternatives by removing some of the assumptions of BEM theory. On the higher end of the spectrum, the large-eddy simulation actuator line method (LES-ALM;Troldborg et al, 2007;Churchfield and Lee, 2012;Churchfield et al, 2012;Wang et al, 2019) is currently the main approach for the modeling of wakes, including the hot topic of wind farm control (Fleming et al, 2013;Gebraad et al, 2016).…”