Wind turbines are often sited together in wind farms as it is economically advantageous. However, the wake inevitably created by every turbine will lead to a time-varying interaction between the individual turbines. Common practice in industry has been to control turbines individually and ignore this interaction while optimizing the power and loads of the individual turbines. However, turbines that are in a wake experience reduced wind speed and increased turbulence, leading to a reduced energy extraction and increased dynamic mechanical loads on the turbine, respectively. Neglecting the dynamic interaction between turbines in control will therefore lead to suboptimal behaviour of the total wind farm. Therefore, wind farm control has been receiving an increasing amount of attention over the past years, with the focus on increasing the total power production and reducing the dynamic loading on the turbines. In this paper, wind farm control-oriented modeling and control concepts are explained. In addition, recent developments and literature are discussed and categorized. This paper can serve as a source of background information and provides many references regarding control-oriented modeling and control of wind farms.

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.

Abstract. In this paper, the potential of Dynamic Induction Control (DIC), which has shown promising results in recent simulation studies, is further investigated. When this control strategy is implemented, a turbine varies its induction factor dynamically over time. In this paper, only periodic variation, where the input is a sinusoid, are studied. A proof of concept for this periodic DIC approach will be given by 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.

Abstract. 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.

Abstract. 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 atmosphericlike 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. IntroductionBecause of the increasing energy consumption and the expansion of renewable energy the demands on wind energy converters are constantly increasing. Especially the development of new offshore wind turbines with higher energy production are of very high importance. As part of the Innwind.eu project, which had the overall objectives of the high performance innovative design of a beyond-state-of-the-art 10-20 MW offshore wind turbine and hardware demonstrators of some of the critical components, new mechanisms for active and passive rotor load control were developed. As the wind fields of the atmospheric boundary layer (ABL) acting on the rotor and the turbine are turbulent as shown in [1], the validation of these new concepts have to take place under controlled turbulent conditions. We present a wind tunnel experiment, which combines a model wind turbine equipped with these new control designs and a turbulent inflow generated by an so called active grid. The active grid is an instrument allowing us to generate turbulence with a wide range of different behaviour, additionally such turbulent flows can be repeated quite accurately. So it is possible to validate these new concepts using different turbulent inflow conditions.

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.

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.

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.