In recent studies, the effectiveness of different so-called wake mixing strategies has been assessed in terms of wind farm power maximization. These studies show that by dynamically varying the pitch angles of a wind turbine, wake mixing can be enhanced to increase the overall power production of a wind farm. However, such strategies also increase the loads experienced by the turbine, which may disqualify such methods as viable wind farm control strategies. In this paper, an extensive analysis of the load effects of two specific wake mixing strategies, Dynamic Induction Control (DIC) and the helix approach, is presented. The damage equivalent load of critical components such as the turbine blades and tower is assessed, and the risk of fatigue damage on the blade pitch bearings is determined. This paper therefore contributes to determining the implementability of such wake mixing strategies in wind farms of the future.

Individual pitch control (IPC) is an effective and widely used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this situation may lead to an emergency shutdown and increased maintenance costs. In this paper, a fault-tolerant individual pitch control (FTIPC) scheme is developed to accommodate these faults in floating offshore wind turbines (FOWTs), based on a Subspace Predictive Repetitive Control (SPRC) approach. To fulfill this goal, an online subspace identification paradigm is implemented to derive a linear approximation of the FOWT system dynamics. Then, a repetitive control law is formulated to attain load mitigation under operating conditions, both in healthy and faulty conditions. Since the excitation noise used for the online subspace identification may interfere with the nominal power generation of the wind turbine, a novel excitation technique is developed to restrict excitation at specific frequencies. Results show that significant load reductions are achieved by FTIPC, while effectively accommodating blade and actuator faults and while restricting the energy of the persistently exciting control action.

Individual Pitch Control (IPC) is a well-known and, in normal operating conditions, effective approach to alleviate blade loads in wind turbines. However, in the case of a Pitch Actuator Stuck (PAS) type of fault, conventional IPC is not beneficial since its action is disturbed by the failed pitch actuator. In this paper, a Subspace Predictive Repetitive Control (SPRC)-based IPC is proposed to implement a Fault Tolerant Control (FTC) strategy for Floating Offshore Wind Turbines (FOWTs) affected by PAS faults. In particular, an online subspace identification step is first carried out to obtain a linearized model of the FOWT system in faulty condition. The identified FOWT system is then used to develop a repetitive control law. Consequently, the adaptive repetitive control solution is implemented on the remaining healthy pitch actuators, in order to accommodate the PAS fault. Results show the developed SPRC approach allows to accommodate the PAS faults, achieving a considerable reduction of the blade loads in combination with lower pitch activities for the healthy actuators. This allows to continue power production and postpone maintenance operations, thus reducing the OM costs.

Dynamic wind farm control is a new strategy that aims to apply time-varying, often periodic, control signals on upstream wind turbines to increase the wake mixing behind the turbine. As a result, wake recovery is accelerated, leading to a higher power production of downstream turbines. As the amount of interest in dynamic control strategies for wind turbines in a wind farm is increasing, different approaches are being proposed. One such novel approach is called Dynamic Individual Pitch Control (DIPC). In DIPC, each blade pitch angle of a turbine is controlled independently to dynamically manipulate the direction of the thrust force vector exerted on the wind. Hence, the direction of the wake is varied, inducing wake mixing without significant thrust force magnitude variations on the rotor. In this paper, the effectiveness of different variations in the thrust direction are evaluated and compared using Large Eddy Simulation (LES) experiments.

As wind turbines in a wind farm interact with each other, a control problem arises that has been extensively studied in the literature: how can we optimize the power production of a wind farm as a whole? A traditional approach to this problem is called induction control, in which the power capture of an upstream turbine is lowered for the benefit of downstream machines. In recent simulation studies, an alternative approach, where the induction factor is varied over time, has shown promising results. In this paper, the potential of this dynamic induction control (DIC) approach is further investigated. Only periodic variations, where the input is a sinusoid, are studied. A proof of concept for this periodic DIC approach will be given by the execution of scaled wind tunnel experiments, showing for the first time that this approach can yield power gains in real-world wind farms. Furthermore, the effects on the damage equivalent loads (DEL) of the turbine are evaluated in a simulation environment. These indicate that the increase in DEL on the excited turbine is limited.

Wind farm control using dynamic concepts is a research topic that is receiving an increasing amount of interest. The main concept of this approach is that dynamic variations of the wind turbine control settings lead to higher wake turbulence, and subsequently faster wake recovery due to increased mixing. As a result, downstream turbines experience higher wind speeds, thus increasing their energy capture. In dynamic induction control (DIC), the magnitude of the thrust force of an upstream turbine is varied. Although very effective, this approach also leads to increased power and thrust variations, negatively impacting energy quality and fatigue loading. In this paper, a novel approach for the dynamic control of wind turbines in a wind farm is proposed: using individual pitch control, the fixed-frame tilt and yaw moments on the turbine are varied, thus dynamically manipulating the wake. This strategy is named the helix approach because the resulting wake has a helical shape. Large eddy simulations of a two-turbine wind farm show that this approach leads to enhanced wake mixing with minimal power and thrust variations.

A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine. An open-jet wind tunnel with an innovative active grid is employed to generate reproducible turbulent wind conditions. A significant load reduction with limited actuator duty is achieved even under these high turbulent conditions. Furthermore, it will be demonstrated that SPRC is able to adapt to changing operating conditions.

A wind tunnel experiment is presented which combines the use of controlled turbulent inflow conditions and a two-bladed model wind turbine utilizing a new control strategy called subspace predictive repetitive control (SPRC). The validation of the performance of SPRC was made under turbulent inflow conditions generated by an active grid. The 3m × 3m active grid is used in this experiment using a unique method to generate reproducible atmospheric- like turbulent wind fields to act on a medium sized model wind turbine. This contribution is focussing on the detailed description of the experiment and its components and the analysis of the turbulent inflow by means of one and two point statistics. Exemplarily the impact of the new control strategy to the generated turbulent test cases are discussed.

To reduce the cost of wind energy, it is essential to reduce loads on turbine blades to increase lifetime and decrease maintenance cost. To achieve this, Individual Pitch Control (IPC) received an increasing amount of attention in recent years. In this paper, a data-driven IPC algorithm called Subspace Predictive Repetitive Control (SPRC) is used to alleviate periodic loads on a scaled 2-bladed wind turbine in turbulent wind conditions. These wind conditions are created in an open-jet wind tunnel with an active grid, enabling unique reproducible high turbulent wind conditions. Significant load reductions are achieved even under these high turbulent conditions.