The Helix approach is a dynamic control method that mitigates wind turbine wake effects by pitching the turbine blades individually. In current literature the method is mainly researched with a high-fidelity actuator line model (ALM). Because of the computational constraints imposed by this model, the effect of the Helix has not been researched for turbine arrays longer than 3. To investigate the effects of the Helix on a farm size scale, a lower fidelity model capturing the Helix is developed in this research. This model is based on a uniform actuator disk model and is called the H-ADM. The H-ADM is validated in laminar conditions at a fine resolution by comparing wake recovery to the ALM. The H-ADM captures both the additional wake mixing and wake displacement caused by the Helix. Furthermore, the H-ADM correctly predicts differences between the counterclockwise (CCW) and clockwise (CW) Helix. Due to modelling assumptions, the H-ADM can run simulations at a coarser resolution and with a smaller time-step than the ALM. Computational gains are achieved with the H-ADM as a result. This research shows that similar simulations are executed over 640 times faster for the H-ADM compared to ALM. The model is used to gain a deeper understanding of the mechanisms causing differences between the CCW and CW Helix. It turns out these differences fade away if no rotational force is exerted by the wind turbine. Finally, the computational gains of the H-ADM are leveraged by applying the Helix on the first row of a large scale wind farm with 48 turbines. For the considered farm the CCW Helix seems to outperform the CW Helix in terms of total power gains.

The development of offshore wind technology has progressed rapidly since the first offshore wind farm came to birth in 1991. During the last three decades, offshore wind has become an economically attractive option of renewable energy source for many countries to fulfill their commitment to combat the climate crisis. However, an open challenge remains in offshore wind, namely the aerodynamic interactions between multiple turbines, referred to as wake effects. When the upstream turbines extract energy from wind, they generate the wake with low velocity and high turbulence level on the downstream turbines, which substantially reduces the overall wind farm performance. The annual power production loss due to wake effects is generally 10 - 40%, which limits the reduction of the levelized cost of electricity for offshore wind energy.

Therefore, developing effective wind farm control strategies which could mitigate the wake effect has become an emerging research area in the past decade. Recently, a novel wind farm control strategy called the Helix approach is proposed. The Helix approach adopts the individual pitch control technique to dynamically deform the wake into the helical shape, which induces wake instability and thereby stimulates wake recovery. This control strategy has demonstrated promising potential to mitigate the wake effect and to enhance the wind farm performance. To date, the Helix approach employs single harmonic pitch signals. However, more complex and higher-harmonic signals to potentially improve the effectiveness of the Helix approach have never been explored. Therefore, the purpose of this master thesis is to explore the potential of using multi-sine pitch profiles to further enhance the wake mixing.

The high-fidelity aeroelastic simulator OpenFAST with its recent free vortex wake code is adopted to simulate the dynamic wake evolution. A Fourrier stability analysis is used to quantitatively identify the wake breakdown position. According to the simulation results for the multi-sine pitch profiles, the wake breaks down at 1.75 D, which is earlier than the one at 2.50 D for the original single-sine Helix strategy. The earlier wake breakdown indicates the potential of faster wake recovery, which is required to be validated by the higher-fidelity model in the future studies.