As the world shifts towards sustainable energy, wind power stands out as a compelling solution to combat climate change. Advances in wind technology, particularly larger turbines, have made it more cost-competitive. Taller towers and bigger rotors allow modern turbines to access better wind resources at greater heights, capturing more power and reducing costs.

Yet, achieving economically viable designs for taller towers requires reducing material usage. While this reduction decreases weight and costs, it also makes towers more prone to fatigue loading and material damage due to their inherent flexibility. This flexibility can lead to resonant behaviour, particularly in variable-speed wind turbines, exacerbated by factors like rotor mass and aerodynamic imbalances. Such resonances can cause material degradation, structural failure, and thus higher maintenance costs. The challenge of minimizing tower motion in wind turbines has been addressed with both active and passive conventional control strategies, largely for fore-aft tower movement. The current advanced control methods for managing tower side-side periodic loads rely on passive frequency-skipping techniques. However, there is a need for more advanced control methods capable of actively cancelling these loads. Therefore, this thesis aims to develop an efficient convex model predictive control method known as the velocity-based quasi-Linear Model Predictive Control (qLMPC) scheme, specifically designed for actively cancelling side-side periodic loads. This approach omits the need for extensive equilibrium input and state vectors, reducing memory usage while effectively capturing the system’s nonlinear behaviour.

This study uses a Model Demodulation Transformation (MDT) technique to derive wind turbine
tower dynamics, focusing on the real and imaginary parts of the tower top displacement and velocity signals. The goal is to minimize these outputs, reducing tower motions caused by periodic side-side forces. The model includes a wind turbine aerodynamic model, linearized through velocity-based linearization and controlled using a velocity-based qLMPC controller.

Optimization weights are carefully tuned to balance minimizing power output disruptions with mitigating disturbances from side-side periodic loads. Five simulation cases, with varying wind speeds and load profiles, were conducted. The results show the algorithm’s simplicity, efficiency, and suitability for online applications. In all cases, the optimization objective is achieved, with the tower top position and velocity converging to zero, demonstrating the control algorithm’s effectiveness in mitigating side-side periodic loads.