Wind farm control is an area of research that looks to maximise the performance of individual but most importantly the collective performance of wind farms. The performance of turbines located in a wind farm is heavily dependent on the effects of turbine wakes, and improving the efficiency of wind farms by mitigating the wake effects is an ongoing and promising field of research. Simulating the wake effects of a turbine is generally done with computationally expensive physics based modelling techniques like CFD simulations. The computational complexity of these models limits the scope of optimisation that can be performed on wake mitigating controls strategies such as dynamic induction control. A substitute engineering model like the free vortex wake model could provide better insight into dynamic induction control by virtue of its computational efficiency allowing a wider optimisation parameter space. In this thesis an optimisation framework based on a 2D implementation of the free vortex wake model and genetic algorithm optimisation is used to investigate dynamic induction control for a simple two turbine wind farm beyond the research that has been done up to this point. While yet to be confirmed with higher fidelity simulations, initial results corroborate earlier findings for simple dynamic induction signals and additionally indicate that dynamic induction control could benefit from multiple harmonics. The optimisation framework achieves its goal in allowing a wider parameter space to be searched for optimisation, even on consumer grade desktop hardware, and shows potential as a tool for further investigation of dynamic induction control.

This thesis investigates the concept of turbine repositioning to enhance energy production in floating wind farms. Due to the dense deployment of floating turbines, downstream units could potentially experience reduced wind speeds caused by the wakes of upstream turbines, leading to decreased power output—an effect known as the wake effect. To address this, methods such as power de-rating and yaw-based wake redirection have been extensively studied. Notably, for floating wind farms, the ability of turbine bases to move within a certain range has prompted the proposal of turbine repositioning as a novel wake mitigation strategy. This study delves into optimal control strategies for turbine repositioning, with a particular emphasis on manipulating rotor yaw angles. It introduces two primary repositioning strategies: static repositioning, suitable for farms with relatively slack mooring lines, and dynamic repositioning, for those with tighter lines. Alongside, the research proposes optimization methods to identify the optimal control sequences for each repositioning strategy. Lastly, by analyzing rotor yaw angle control sequences in the frequency domain, this study distinguishes the frequency component crucial for repositioning turbines from that steering the wakes. The findings provide significant insights into enhancing the cost-effectiveness of power production in floating wind farms through effective wake interaction management.

This thesis investigates the concept of turbine repositioning to enhance energy production in floating wind farms. Due to the dense deployment of floating turbines, downstream units could potentially experience reduced wind speeds caused by the wakes of upstream turbines, leading to decreased power output—an effect known as the wake effect. To address this, methods such as power de-rating and yaw-based wake redirection have been extensively studied. Notably, for floating wind farms, the ability of turbine bases to move within a certain range has prompted the proposal of turbine repositioning as a novel wake mitigation strategy. This study delves into optimal control strategies for turbine repositioning, with a particular emphasis on manipulating rotor yaw angles. It introduces two primary repositioning strategies: static repositioning, suitable for farms with relatively slack mooring lines, and dynamic repositioning, for those with tighter lines. Alongside, the research proposes optimization methods to identify the optimal control sequences for each repositioning strategy. Lastly, by analyzing rotor yaw angle control sequences in the frequency domain, this study distinguishes the frequency component crucial for repositioning turbines from that steering the wakes. The findings provide significant insights into enhancing the cost-effectiveness of power production in floating wind farms through effective wake interaction management.