The global demand for renewable energy is driving the rapid growth of the wind energy sector, with wind farms increasingly preferred over stand-alone turbines due to their operational and economic benefits, such as reduced deployment costs, operational efficiencies, and minimized land use. Effective wind farm operation depends on optimizing energy extraction, necessitating accurate energy yield predictions. Traditional wind turbine design focuses on maximizing individual performance, often overlooking interactions within wind farms that can lead to suboptimal overall performance due to wake effects. Wakes are characterized by reduced wind speeds and increased turbulence downstream of turbines, negatively impacting the performance of downstream turbines. Managing these wakes is crucial for improving overall wind farm efficiency.

Wind Farm Flow Control (WFFC) aims to enhance wind farm performance by managing these wake effects through control strategies. This thesis addresses the need for integrating advanced control techniques, such as Individual Pitch Control (Helix Approach), into engineering wake models to improve wind farm simulations and real-time control applications.

To achieve this, the research identifies the most suitable wake model by evaluating various models implemented in PyWake, selecting the Super Gaussian model for its superior accuracy in representing the wake behavior in the whole region. A robust calibration framework is developed, balancing precision and computation time. The thesis investigates the stability of the calibration process, addressing common optimization challenges such as local minima, and identifies strategies to ensure the global optimum configuration.

The sensitivity of model parameters to different inflow conditions is analyzed, allowing the calibration process to be generalized across a range of wind speeds within the partial load region of the power curve. This generalization reduces the need for frequent recalibration, streamlining the process. The research further explores the responsiveness of different control configurations, with a focus on the helix approach, to establish correlations between control signal amplitudes and model parameters.

The newly extended model incorporates helix approach information through parameter calibration for varying pitch angle amplitudes, demonstrating a polynomial relationship between model parameters and control signal amplitudes. This integration reduces the calibration requirement to a single procedure, enhancing model adaptability.

Finally, the extended model is tested for optimizing online wind farm performance across different configurations. The helix approach shows significant improvements in reducing wake losses and increasing overall wind farm efficiency, particularly in turbine clusters. Comparisons with traditional "greedy control" highlight the helix approach's advantage, with power output increasing by up to 25% in clusters of five turbines when upstream turbines utilize helix control.

This thesis contributes to the field by providing a comprehensive approach to wind farm modeling and control, facilitating the practical application of advanced control strategies. The integration of control data into engineering wake models not only enhances calibration processes but also broadens their practical applications, paving the way for more efficient and sustainable wind farm operations.