We investigate the tradeoffs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.

Wind speeds can be measured remotely from the ground with lidars. Their estimates are accurate for mean speeds, but turbulence leads to measurement errors. We predict these errors using computer-generated data and compare lidar measurements with data from a meteorological mast. The comparison shows that deviations depend on wind direction, measurement height, and wind conditions. Our method to reduce the measurement error is successful when the wind blows aligned with one of the lidar beams.

Three diffuser augmented wind turbines with separate diffuser concepts were designed by carrying out a series of numeric investigations and a self-developed design optimization process. Single element diffuser was found as the optimum concept based on performance increase and design complexity. The results also highlighted the applicability of diffusers on existing bare rotor geometries. The turbine performances were evaluated by using an edited version of BEM, which was adopted to DAWTs.

Wind turbine rotors are usually designed to maximize power performance accepting whatever loading results. However from the most basic wind turbine theory, actuator disc theory, two other optimization paths are demonstrated which may lead to more cost effective technology – the low induction rotor where an expanded rotor diameter and some extra power is achieved without increasing the blade root bending moment and the secondary rotor which can provide a very low torque and low cost drive train.