Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

Abstract

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

“…The turbines attempts to align itself with the local inflow direction to optimize the power production, where the presence of wake effects may alter the local flow wind direction. Such behaviour was also described by McKay et al (2013) and shown in Hulsman et al (2019), where a optimization based on surrogate models showed that the second turbine should indeed align itself with the local wind direction. Archer and Vasel-Be-Hagh (2019) used LES to also show how turbines deep inside the farm could be intentionally yawed for improved performance.…”

Analytical model for the power–yaw sensitivity of wind turbines operating in full wake

“…The turbine attempts to align itself with the local inflow direction to optimize the power production, where the presence of wake effects may alter the local flow wind direction. Such behavior was also described by McKay et al (2013) and shown experimentally by Bartl et al (2018) as well as through the use of surrogate models based on high-fidelity simulations in Hulsman et al (2019), where the second turbine should indeed align itself with the local wind direction to optimize power production. Archer and Vasel-Be-Hagh (2019) used large-eddy simulations (LESs) to also show how turbines deep inside the farm could be intentionally yawed for improved performance.…”

“…A common form of wind farm control for power optimization is wake steering, in which a wake can be redirected away from a downstream turbine by inducing a yaw misalignment in the upstream turbine. Numerous studies on wake redirection have been performed by Fleming et al (2016), Gebraad et al (2016), Gebraad et al (2017), Jiménez et al (2010), Bossanyi (2018), and Munters and Meyers (2018), showing improved annual energy production in wind farms ranging between 2 % and 8 %. These investigations often assume a constant value of α to determine the trade-off between power losses due to yawing the upstream turbine and the power gain of the downstream turbine.…”

Review of wake management techniques for wind turbines

“…While many studies have performed proofs of concepts using only two or three turbines, 26,33,40,47,102,106,109,111,112,119,123,125,131,132,134,138,143,151,161–164 wake steering is more complicated in a wind farm. Fortunately, and in contrast to AIC, many of the lessons learned with a column of turbines still apply in larger arrays.…”