Robust active wake control in consideration of wind direction variability and uncertainty

Abstract

Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability of current environmental conditions in the field and the uncertainty of their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with 5 wind direction measurements recorded at an onshore met mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines.Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimise the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that 10 the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

“…More recently, there have been positive developments in the field of real-time model adaptation, using more sophisticated estimation algorithms that attempt to balance accuracy with computational efficiency (e.g., [28], [29]). In terms of optimization, for steady-state surrogate models, a gradient-based or nonlinear optimization algorithm is typically employed to determine the optimal steady-state control settings for the wind farm (e.g., [8], [30], [31]). For dynamic surrogate models, typically predictive control methods are followed to yield an optimal control policy, which typically is a time-varying solution (e.g., [25], [32], [33]).…”

“…Hence, a robust optimization approach is followed. In this case, we use the approach from Rott et al [31], in which the yaw angles are optimized for a probability distribution of wind directions, rather than one deterministic wind direction. The optimization is formulated as follows,…”

“…Note that there is some switching behavior at t = 1200 s in the optimal yaw angles due to the change in sign of the estimated wind direction. While the robust optimization approach should account for this [31], it is expected that the standard deviation for ρ in the optimization was too low. This should be investigated in future work.…”

Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes

“…Values in the order of 6° dominate and indicate that even within comparatively short time periods of only 10 minutes, larger fluctuations of the inflow direction commonly occur. That the variability of the inflow direction for 5‐minute time periods at the test site can be sufficiently approximated by means of a Gaussian distribution has been shown by Rott et al using the Kolmogorov‐Smirnov test. Of all data sets recorded during the field campaign, 71% of the 5‐minute intervals passed.…”

Model-based closed-loop wind farm control for power maximization using Bayesian optimization: a large eddy simulation study

“…After model adaptation, the surrogate model is assumed to accurately capture the current conditions inside the farm. A robust optimization approach is then followed based on the work from Rott et al [19], in which the yaw angles are optimized for a probability distribution of wind directions, rather than one deterministic wind direction. The to-be-maximized cost function is:…”