Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to reduce these losses to a minimum is yaw-based wake steering. This thesis investigates the feasibility of this technique by calibrating a surrogate model called the FLOw Redirection and Induction in Steady-state (FLORIS) model on a data set from the Lillgrund wind farm and using it to estimate the potential energy gain. The data set available is processed methodically to remove outliers and erroneous data points, resulting in a reliable and useful data set. It is used to obtain free stream wind conditions per time step and relate those to power measurements. The data set is consequently used to calibrate the tuning parameters of the FLORIS model. The calibration is done using a newly proposed method that determines the tuning parameters per combination of wind speed and turbine spacing. A difference with commonly applied calibration methods is that power measurements are used instead of predicted powers or flow field data from high-fidelity models. The performance of the calibrated model is tested through multiple uncertainty analyses. It is found that the model has a significant bias but low uncertainty by comparing the predicted wake losses with measured wake losses. This bias can potentially be reduced if atmospheric stability is taken into account. With the bias and uncertainty quantified, the FLORIS model is used to optimize the annual energy production of the Lillgrund wind farm by finding the ideal yaw angles for specific inflow conditions. A significant energy gain can be achieved when the optimal yaw angles are determined deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is considered, showing that these yaw angles are not robust in terms of performance under uncertainty. More robust yaw angles can be obtained when the input uncertainty is taken into account during the yaw optimization. The energy gain achievable with these more robust yaw angles is approximately 3.4%. Therefore, it can be concluded that achieving an energy gain using yaw-based wake steering is feasible for the Lillgrund wind farm.

Wind energy is one of the most promising sources of renewable energy and has experienced a very rapid growth in the last 15 years. In order to maintain and sustain this growth wind energy needs to be competitive with the other sources of energy, especially with fossil fuels. A major factor keeping the competitiveness of wind energy is the sustained investment made on research and innovation. Active wake wind farm control is part of the research effort that aims to reduce the cost of producing wind energy; these new control strategies have the goal of reducing wind turbine wakes interactions in order to increase the power production and to minimize loads and fatigue. Literature shows good potential for these control strategies; however most of the studies have been made in a virtual environment through the use of computer simulations and as a consequence there is a lack of real field measurement data analysis that could confirm the potential of these strategies. The French National Research Agency SMARTEOLE project implemented two wind farm control strategies in an operating wind farm called La Sole du Moulin Vieux and owned by Engie Green in order to assess the effects that the applied innovative wind farm control strategies have on the blade loads, particularly in terms of blade fatigue. The two wind farm control strategies applied are the curtailment strategy and the yaw-control strategy. It needs to be noted that due to the high sensitivity of loads and fatigue to inflow conditions and due to the limited amount of data available the results observed should be considered as general trends very specific for this particular wind turbine set up and location. In the curtailment strategy the upstream turbine SMV6 loads decreased up to 33% (at 9 m/s) in the flapwise direction and up to 67% (at 8 m/s) in the edgewise direction. Fatigue loads decreased up to 22% (at 9 m/s) in the flapwise direction and up to 2% (at 9 m/s) in the edgewise direction. In the downstream turbine no clear trend has been detected due to the high uncertainty in the results. Most likely low amount of data and increased turbulence in the downstream turbine led to this uncertainty in the trends. In the yaw-control strategy loads showed a decrease up to 8% (at 9 m/s) in the flapwise direction loads and up to 72% (at 9 m/s) in the edgewise direction loads for the upstream turbine SMV6. Fatigue also decreased up to 19% (at 11 m/s) in the flapwise direction loads and up to 1.3% (at 10 m/s) in the edgewise direction for the upstream turbine. The downstream turbine SMV5 showed no significant reductions in the flapwise loads and fatigue when the whole wake was analyzed but, when the full-wake only was analyzed, a significant increase in loads and power produced, without any significant increase in fatigue loads have been observed. This underlines the potential of the yaw-controlled strategy to increase power production without significantly increasing fatigue.

Due to the limited availability of sites on both land and sea, there is a need to maximize the power density of wind farms whilst limiting the adverse wake effect between the wind turbines that cause power losses and/or increased load cases. The study focuses on analyzing the potential of combining yaw-based wake-steering and constant blade pitch control down-regulation to mitigate the detrimental wind turbine wake interactions. Data-driven surrogate models based upon polynomial chaos theory have been used to model the statistical distributions of the wind farm power production and short-term Damage Equivalent Loads (DEL) in function of the control inputs. The model calibration data has been generated for a range of control settings for two V27 wind turbines aligned with the wind direction through large eddy simulations using the flowsolver EllipSys3D and the aeroelastic code Flex5. A power-based optimization with DEL constraints has been performed and median power gains ranging from +1% to +3% have been observed at close spacings depending on the severity of the imposed constraints. It was identified that larger power gains corresponded to an increase in DEL. At larger spacings, the wind farm control strategies shows limited performance increases and revert to baseline operations.