The offshore wind energy sector has experienced remarkable growth in recent years, driven by increasing demand for renewable energy sources and technological advancements. As wind farms expand and turbine sizes increase, challenges associated with wake effects and turbine interactions become more pronounced. Therefore, wind farm control strategies have been developed to mitigate the negative effects of wakes, consequently enhancing overall power production or reducing fatigue loads. The helix approach has emerged as a promising method for controlling wake dynamics, shaping the wake into a helical pattern to increase wake mixing further downstream.
In this context, phase synchronisation has emerged as an extension of the helix approach, simultaneously applying the helix on up- and downstream turbines. By leveraging the existing helical periodic components induced by the upstream turbine, phase synchronisation can initiate the helix at the downstream turbine with less pitch action. % while improving overall power performance.
However, power is still compromised at the upstream turbine due to the execution of the helix. Consequently, the potential for improvement arises with the integration of phase synchronisation onto the meandering effect—a phenomenon often already present at the downstream turbine. This effect yields benefits similar to those of the helical wake concerning wake recovery, offering the potential to improve overall power performance without power loss at the upstream turbine.
%
This thesis has developed a phase synchronisation method to track the time-varying excitation frequency inherent to the meandering effect. Enhancements were made to a SOGI-PLL, enabling the extraction of phase, amplitude, and bias from periodic loads, which forms the basis of our closed-loop control strategy for the downstream wind turbine. The improvements led to the development of the ESOGI- and MSTOGI-HTan-QT2PLL methodologies, which address internal disturbances caused by offsets and enhance system stability, while accurately tracking of the phase during phase jumps and frequency steps and ramps.
The methodologies were tested at different levels of fidelity. First, a low-order study validated the effectiveness of the proposed method during uncertain conditions, which showed better performance of the MSTOGI-HTan-QT2PLL methodology. Then, the results of the higher-fidelity experiments demonstrated that our method could successfully amplify the meandering effect without loss in power, while attenuation led to reduced fatigue loads with minimal power loss. These results show the potential of integrating phase synchronisation on the meandering effect.

The clustering of wind turbines in a wind farm results in overall efficiency losses as downstream wind turbines operate in the wake of their upstream neighbours. Wind farm flow control (WFFC) strategies have emerged to reduce these wake effects with the goal of maximising overall performance. Dynamic induction control (DIC) aims to enhance the wake breakdown and restore the wake's energy content through dynamic thrust variations. The control signals are often found through the economic model predictive control (EMPC) method, which relies on an internal model to incorporate future system behaviour in the determination of the next optimal control input. These models are designed to capture the most dominant wake characteristics while remaining computationally efficient. We employ the two-dimensional free-vortex wake (FVW) model presented from [1], which models the wake through vortex element pairs released from the edges of the actuator disc. The power of the two-turbine wind farm is maximised through EMPC, improving performance by 9.64\% over greedy control simulations. However, the EMPC method inherits finite horizon effects, resulting in large control horizons to optimise. In this study, we address these limitations by employing an absolute sine parameterisation in the FVW model to limit the finite horizon effects and reduce the dimension of the optimisation problem. The significant dimension reduction allows for a grid search to find the optimal infinite horizon steady-state solution, improving the mean steady-state performance by 2.43\% over the baseline results from [1]. Additionally, we focus on converging towards this optimum through finite horizon EMPC optimising over the amplitude and offset. Grid search analyses reveal sensitivity towards initialisation due to the appearance of local minima around the infinite horizon optimum. A maximum success rate is realised for very large control horizons, maximising the probability of converging towards the infinite horizon optimum. Accounting for the inherited system delay in the objective function also realises a maximum success rate but for shorter control horizons, which significantly decreases the simulation time. The final controller design terminates simulations ten times faster through the absolute sine parameterisation compared to the baseline simulation from [1] while maximising the probability of convergence towards the infinite horizon optimum.