To achieve net-zero greenhouse gas emissions by 2050, the Global Wind Energy Council (GWEC) emphasizes the need for a significant expansion in global wind power capacity. A key factor of this growth is the upscaling of wind turbines, which increases the swept area and exposes the blades to higher wind speeds at higher altitudes, thereby increasing energy yield. However, this upscaling introduces significant control challenges. Larger wind turbines increase aeroelastic complexity, with stronger nonlinear dynamics arising from increased blade flexibility and more significant wind shear across the expanded rotor diameter. Additionally, the increased rotor inertia delays the system response, making rotor speed regulation more difficult under varying wind conditions.

This thesis proposes an adaptive closed-loop Subspace Predictive Control (SPC) framework designed in an attempt to handle the complexity of larger, nonlinear wind turbines. Closed-loop SPC is a direct Data-Driven Predictive Control (DDPC) method that does not rely on explicit state-space modeling, but instead uses measured input-output data to predict future outputs and compute optimal control action. For the optimal control action, it sets up a receding horizon optimization that regulates above-rated rotor speed. This thesis focuses on the above-rated region, where aeroelastic complexity becomes more pronounced due to higher wind speeds and greater wind speed variations, posing significant control challenges.

To capture time-varying and nonlinear behavior more effectively, the closed-loop SPC incorporates Recursive Least Squares (RLS). The controller adapts to time-varying conditions through online parameter estimation using RLS, which updates a locally linear model in real time. Three RLS variants are examined: standard RLS without forgetting, RLS with exponential forgetting, and RLS with directional forgetting. Standard RLS weighs all past data equally, which may be effective when the system dynamics remain stationary but limits adaptability to changing conditions. Exponential forgetting addresses this by placing more weight on recent data, improving adaptiveness, but at the potential cost of losing parameter estimation accuracy in less-excited directions. Directional forgetting refines this further by applying forgetting selectively along the directions of incoming data, preserving excitation in recently unexcited directions and enhancing estimation robustness.

To reduce the phase lag introduced by increased rotor inertia, wind preview information is incorporated into the closed-loop SPC as a feedforward signal. This wind preview is included in the receding horizon optimization problem, enabling the controller to anticipate and proactively respond to upcoming wind changes. Additionally, the wind preview is demonstrated using more realistic measurements obtained through a LIDAR simulator.

The adaptive closed-loop SPC is validated on the DTU 10 MW reference turbine using QBlade, a high-fidelity wind turbine simulator. Various wind scenarios, including gusts, ramps, and turbulent inflow, are evaluated with and without wind preview feedforward. Results demonstrate that the inclusion of wind preview significantly improves rotor speed tracking performance and reduces pitch activity. This improvement is also observed when more realistic LIDAR wind measurements are used in simulations with a turbulent wind field. In the conducted wind cases, among the RLS-based adaptive closed-loop SPC strategies, exponential forgetting combined with wind preview consistently outperformed the other RLS approaches across all scenarios evaluated in this thesis. These findings demonstrate that introducing adaptiveness through forgetting, together with feedforward wind information, can enhance closed-loop SPC performance in rated rotor speed tracking.

Data-Driven Predictive Control (DDPC) has emerged as a promising alternative to Model Based Control (MBC), enabling direct control using Input-Output (I/O) data without requiring explicit model identification. This thesis advances DDPC by bridging the gap between theoretical developments and real-world implementation. The study focuses on four algorithmic variants: Subspace Predictive Control (SPC), Closed Loop Subspace Predictive Control (CL SPC), Recursive Closed Loop Subspace Predictive Control (R-CL SPC), and Constrained Recursive Closed Loop Subspace Predictive Control (CR-CL SPC), to enhance adaptability and ensure real-time feasibility.

A key insight in computational efficiency is the R-CL SPC algorithm, which updates system parameters online using recursive least squares estimation combined with Givens rotations. This reduces computational complexity by avoiding large-scale matrix inversions at each time step. Additionally, the CR-CL SPC variant introduces constraint handling via a Quadratic Programming (QP) solver, enabling input and output constraints.

These algorithms’ performances were evaluated through simulation studies and real-time experiments on a piezo-actuated beam setup, where the control objective was to suppress the first two natural vibration modes. The R-CL SPC algorithm demonstrated a strong balance between computational efficiency and control performance, achieving execution times below 0.2 ms while maintaining effective vibration damping. Meanwhile, though at a higher computational cost, CR-CL SPC validated constraint enforcement capabilities.

This thesis demonstrates that adaptive SPC algorithms can be successfully implemented on real-world hardware. The results contribute to the growing knowledge on direct DDPC strategies and provide a foundation for their broader application in real-time, constrained control systems.

The placement of wind turbines in wind farms is economically beneficial to the industry. Consequently, the wake generated by the upstream turbines introduces interactions with downstream turbines that significantly affect power generation. However, it is common practice to control the individual turbines to operate at their optimal settings and neglect the wake effect. Turbines in a wake experience lower wind speeds and an increase in turbulence. This results in lower power generation and increased fatigue loads on the downstream turbine. Wind farm control aims to increase overall power generation and reduce fatigue loads by considering all turbines in the wind farm.
Recently, numerous studies have been conducted to control the wake itself. The helix approach is one such strategy and seeks to initiate better mixing of the wake. For this, it uses IPC to create sinusoidal blade bending moments acting on the rotor disc. This in turn creates a helical structure in the velocity of the wind, allowing it to better mix with the surrounding energy-rich wind. As a result, the waked downstream turbine experiences an increase in power due to the reduced velocity deficit in the wake. An extensive load analysis of the helix method has shown that the rotational frequency of this helical wake can be measured in the bending moments of the waked downstream turbine. To further develop the field of dynamic wake mixing, this work investigated the initialization of the helix method in a downstream turbine by amplifying these signals through synchronization with the incoming helical wake.
This method was tested in simulation environments with increasing levels of fidelity and number of turbines simulated. The results with low fidelity showed that synchronization could be achieved without loss of performance. In addition, less pitch actuation was required to generate large bending moments due to amplification. The final experiment was conducted for a three-turbine setup and was performed in a high-fidelity model using CFD methods. Here it was found that -90 degrees out-of-phase synchronization with the incoming helical wake resulted in a 9.8% increase in power generation in a turbine further downstream. These results demonstrate the clear potential of wake synchronization in a multi-turbine setting. This study, therefore, contributes to the field of dynamic wake mixing by introducing a novel field of wake synchronization.