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Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re-energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two-turbine setup by as much as 15%., Team Jan-Willem van Wingerden, Wind Energy

Two novel approaches to data-driven wind farm control via Koopman model predictive control are presented, both combining thrust and yaw control for yield optimization and power reference tracking. The Koopman framework is used to build prediction models to predict wake effects of upwind on downwind turbines. This paper extends previous work by using yaw in addition to thrust control. The test case is a wind farm consisting of two turbines and wind with constant speed and direction parallel to the main axis of the farm. In closed-loop simulation, the two Koopman model predictive control designs reduce the tracking error considerably with regards to a previously published baseline controller, which used solely axial induction control. It is also demonstrated that this can be achieved with relatively small yaw angles, avoiding mechanical loads acting on turbines operating misaligned to the wind, making this a promising approach for further investigations in 3D medium and high fidelity simulation environments.

In this work, we find a reduced-order model for the wake of a wind turbine controlled with dynamic induction control. We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be investigated and that the model itself can be easily embedded into control-oriented frameworks. After discussing the advantage of forcing the linear system resulting from the algorithm to be conservative (as a consequence of the periodicity of the pitch excitation) and the choice of observables, we describe a procedure for calculating the energy associated with individual modes. The considered data-set is composed of large eddy simulation (LES) results for a single DTU 10 MW wind turbine in uniform flow. Simulations were performed first with baseline control (for reference) and then with the Pulse and the Helix approaches with constant excitation amplitude and different excitation frequencies. The frequencies and energies associated with the resulting modes are discussed., Team Jan-Willem van Wingerden, Wind Energy

Floating wind turbines despite the the potential to harness energy from deep offshore areas where higher average wind speeds face challenges in terms of competitiveness. One approach to raising the competitiveness of a wind farm is to mitigate efficiency losses resulting from the wake effect. This report focuses on the combination of three notable wake effect mitigation strategies: layout optimization, yaw-based wake redirection, and turbine repositioning. A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency. Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly. Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position, https://github.com/Vincer99/VCeriello-Thesis-Code The code used for the content provided in this Master’s thesis is available at this URL., European Wind Energy Masters (EWEM)

As renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

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., Team Jan-Willem van Wingerden

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. Ac, Mechanical Engineering | Systems and Control

Many control strategies have emerged in the field of wind energy to address wake interaction effects and try to maximize wind farm power production. They rely either on wake redirection or on induction control. While wake redirection is easily performed by yawing the turbine, efficient and practically deployable induction control is not as straightforward. Recent studies paved the way with periodic dynamic induction control (PDIC), a strategy that generates pulsatile patterns in the wake and increases wake remixing. This study aims at further investigating the physics behind PDIC through large eddy simulations at high resolution. Results show the wake shear layer destabilization leading to the pulsatile wake. This paper also presents a comprehensive comparison between greedy control, PDIC and static yaw. It focuses both on power and loads at the scale of a pair of in-line turbines. It shows that PDIC significantly increases fatigue loads and leads to moderate power gains, while properly oriented yaw leads to fatigue load reduction on the upstream turbine and noticeable increase of the power production.

Concepts for control of wind farms can be clustered into two distinct concepts, namely wind power plant control and wind farm flow control. Wind power plant control is concerned with the connection to the power system, compliance with grid codes and provision of power system services. This comprises the traditional way of operating a wind farm without consideration of aerodynamic turbine interaction. However, flow phenomena like wake effects can have a large impact on the overall performance of the wind farm. Wind farm flow control considers such aerodynamic phenomena in the wind farm operation. It can be viewed as a new feature that shall be integrated with the existing control functions. The interaction of these different control concepts is discussed in this article, leading to an identification of the challenges whose solutions will contribute to a successful integration of electrical system and aerodynamic aspects of wind farm control.

Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control., Team Jan-Willem van Wingerden, Wind Energy

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., Team Jan-Willem van Wingerden

To accelerate the growth of offshore wind power, its Levelised Cost Of Energy (LCOE) must be reduced. A means of reducing the LCOE of offshore wind power is by mitigating the adverse wake effect by employing Wind Farm Control (WFC) for power maximization. A recent WFC technology is the helix approach, which uses Individual Pitch Control (IPC) to initiate early wake mixing. Research regarding the helix approach is scarce, yet early results suggest that significant power gains are achievable. Simultaneously, increases in wind turbine loads are expected. To aid future researchers in making a trade-off between increased loading and performance, an extensive load analysis is conducted, aiming to analyze and quantify the load impact of the helix approach on the SG 11.0-200 DD offshore wind turbine. A sensitivity analysis is conducted to investigate the influence of the helix parameters on the load impact on the wind turbine tower, blades, main bearing and pitch bearing. It is found that the load impact on all components is mostly sensitive to the pitch amplitude. Most components show some sensitivity to the Strouhal number as well, except for the main bearing. For the blade pitch bearings, an upward and downward trend can be observed for Counter-Clockwise (CCW) and Clockwise (CW) helix directions, respectively, which are related to the pitch frequency. The helix approach is part of the plans for the Hollandse Kust Noord (HKN) wind farm. For this reason, a lifetime fatigue analysis is conducted to quantify the load impact on a wind turbine at the HKN wind farm over its full lifetime for several helix implementations. The fact that the helix approach is only employed under certain conditions is taken into account. It was found that the load impact on the pitch bearings is significant. The load impacts on the tower and main bearing become considerable for helix implementations with a large pitch amplitude. All other components are mildly affected., Mechanical Engineering | Systems and Control

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. Ac, Mechanical Engineering | Systems and Control

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%., Wind Energy

To meet current renewable energy demands, there is a major increase in global wind power capacity. Wind turbines are commonly clustered in wind farms. When operating, wind turbines extract energy from the wind, creating a region characterized by a low velocity, called the wake. The wake negatively impacts the power production of downstream wind turbines. This power loss can be alleviated with wind farm control, where an upstream wind turbine deviates from optimal individual operation to improve the power production of the total wind farm. A novel wind farm control concept is dynamic induction control. While the results of dynamic control look promising, the working principles behind the increase in energy are not yet well understood. This thesis provides a framework to evaluate the transport of kinetic energy into a cylindrical control volume in a wind turbine wake using the instantaneous Navier Stokes energy equation. This approach can provide insight into the time behaviour of kinetic energy transport and can be used in conjunction with the Reynolds Averaged Navier Stokes energy equation. The wake is analyzed using Proper Orthogonal Decomposition to identify the dominant phenomena. The working principle of dynamic induction control is qualitatively described by dividing the wake into three regions: region I, dominated by pressure, region II, dominated by a vortex ring and region III, dominated by turbulence. The peak of transport of kinetic energy occurs in region II, as is confirmed by analysis through the instantaneous Navier Stokes energy equations. Finally, this thesis shows that for a simulation with uniform inflow and where the effect of the nacelle is not modelled, the transition from region II to region III is triggered by the interaction of the vortex ring with an inner vortex ring that forms around the nacelle wake. The nacelle wake is a result of not modelling the nacelle and deemed non-physical, so the ring vortex breakdown location that arises from th, European Wind Energy Masters (EWEM)

The dissertation discusses different wind turbine control strategies that use pitch actuation to decrease the Levelized Cost of Energy (LCoE) of wind farms. This is achieved by either mitigating the loads experienced by individual turbines, or by enhancing wake mixing in turbines located within a wind farm. The latter strategy exploits the fact that wake mixing results in a lower wake deficit, such that turbines located in a wake can generate more power. Wind tunnel experiments are conducted to provide the next step in the development of two control strategies: Subspace Predictive Repetitive Control (SPRC) for load mitigation, and Dynamic Induction Control (DIC) for enhanced wake mixing. These strategies are compared with state-of-the-art technologies to assess their viability as wind turbine control approach. Both technologies prove to be effective in this environment, and achieve results that are similar to or better than state-of-the-art technologies. Finally, this dissertation introduces a novel pitch control technology as an alternative to the DIC approach. This technology, called the Helix approach, uses Individual Pitch Control (IPC) to enhance wake mixing and increase wind farm power generation. A proof of concept of the Helix approach is given through high-fidelity flow simulations., Data-Driven Control

Wind farm control allows coordinated operation of the wind turbines within a wind power plant. However, its development has traditionally been split into the distinct disciplines of power plant control and farm flow control. As variable renewable energies, in particular wind energy, increase their penetration into the power system, grid code requirements become stricter for wind power plants and electricity markets necessarily evolve. In such a context, cross-fertilisation between the two categories of wind farm control, targeting an integrated approach where applicable, becomes a priority. An initial overview on how to further align wind farm flow control to grid and electricity markets participation is provided, including an analysis of capabilities and prospects. Additionally, greater detail is given about an open-access data set serving as market showcases for value demonstration of price-driven operation of wind power plants by means of wind farm flow control.

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%., Wind Energy

The dissertation discusses different wind turbine control strategies that use pitch actuation to decrease the Levelized Cost of Energy (LCoE) of wind farms. This is achieved by either mitigating the loads experienced by individual turbines, or by enhancing wake mixing in turbines located within a wind farm. The latter strategy exploits the fact that wake mixing results in a lower wake deficit, such that turbines located in a wake can generate more power. Wind tunnel experiments are conducted to provide the next step in the development of two control strategies: Subspace Predictive Repetitive Control (SPRC) for load mitigation, and Dynamic Induction Control (DIC) for enhanced wake mixing. These strategies are compared with state-of-the-art technologies to assess their viability as wind turbine control approach. Both technologies prove to be effective in this environment, and achieve results that are similar to or better than state-of-the-art technologies. Finally, this dissertation introduces a novel pitch control technology as an alternative to the DIC approach. This technology, called the Helix approach, uses Individual Pitch Control (IPC) to enhance wake mixing and increase wind farm power generation. A proof of concept of the Helix approach is given through high-fidelity flow simulations., Team Jan-Willem van Wingerden

To meet current renewable energy demands, there is a major increase in global wind power capacity. Wind turbines are commonly clustered in wind farms. When operating, wind turbines extract energy from the wind, creating a region characterized by a low velocity, called the wake. The wake negatively impacts the power production of downstream wind turbines. This power loss can be alleviated with wind farm control, where an upstream wind turbine deviates from optimal individual operation to improve the power production of the total wind farm. A novel wind farm control concept is dynamic induction control. While the results of dynamic control look promising, the working principles behind the increase in energy are not yet well understood. This thesis provides a framework to evaluate the transport of kinetic energy into a cylindrical control volume in a wind turbine wake using the instantaneous Navier Stokes energy equation. This approach can provide insight into the time behaviour of kinetic energy transport and can be used in conjunction with the Reynolds Averaged Navier Stokes energy equation. The wake is analyzed using Proper Orthogonal Decomposition to identify the dominant phenomena. The working principle of dynamic induction control is qualitatively described by dividing the wake into three regions: region I, dominated by pressure, region II, dominated by a vortex ring and region III, dominated by turbulence. The peak of transport of kinetic energy occurs in region II, as is confirmed by analysis through the instantaneous Navier Stokes energy equations. Finally, this thesis shows that for a simulation with uniform inflow and where the effect of the nacelle is not modelled, the transition from region II to region III is triggered by the interaction of the vortex ring with an inner vortex ring that forms around the nacelle wake. The nacelle wake is a result of not modelling the nacelle and deemed non-physical, so the ring vortex breakdown location that arises from th, European Wind Energy Masters (EWEM)

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.

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., Team Jan-Willem van Wingerden

The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non-linearities. The work presented here applies the ideas motivated by non-linear dynamical systems theory—the Koopman Operator—to an innovative algorithm in the context of wind farm systems—Input Output Dynamic Mode Decomposition (IODMD)—to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm., Team Jan-Willem van Wingerden

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., Team Jan-Willem van Wingerden

The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non-linearities. The work presented here applies the ideas motivated by non-linear dynamical systems theory—the Koopman Operator—to an innovative algorithm in the context of wind farm systems—Input Output Dynamic Mode Decomposition (IODMD)—to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm., Team Jan-Willem van Wingerden

Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a non-centralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines.

Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a non-centralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines, Peer Reviewed, Postprint (author's final draft)

Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a non-centralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines., Peer Reviewed, Postprint (author's final draft)

Wind energy is one of the most promising sources of renewable energy and has experienced a very rapid growth in the last 15 years. In order to maintain and sustain this growth wind energy needs to be competitive with the other sources of energy, especially with fossil fuels. A major factor keeping the competitiveness of wind energy is the sustained investment made on research and innovation. Active wake wind farm control is part of the research effort that aims to reduce the cost of producing wind energy; these new control strategies have the goal of reducing wind turbine wakes interactions in order to increase the power production and to minimize loads and fatigue. Literature shows good potential for these control strategies; however most of the studies have been made in a virtual environment through the use of computer simulations and as a consequence there is a lack of real field measurement data analysis that could confirm the potential of these strategies. The French National Research Agency SMARTEOLE project implemented two wind farm control strategies in an operating wind farm called La Sole du Moulin Vieux and owned by Engie Green in order to assess the effects that the applied innovative wind farm control strategies have on the blade loads, particularly in terms of blade fatigue. The two wind farm control strategies applied are the curtailment strategy and the yaw-control strategy. It needs to be noted that due to the high sensitivity of loads and fatigue to inflow conditions and due to the limited amount of data available the results observed should be considered as general trends very specific for this particular wind turbine set up and location. In the curtailment strategy the upstream turbine SMV6 loads decreased up to 33% (at 9 m/s) in the flapwise direction and up to 67% (at 8 m/s) in the edgewise direction. Fatigue loads decreased up to 22% (at 9 m/s) in the flapwise direction and up to 2% (at 9 m/s) in the edgewise direction. In the do, European Wind Energy Masters (EWEM) | Rotor Design Track

Aplicat embargament des de la data de defensa fins al maig 2020, Nowadays, there is rising concern among Transmission System Operators about the declining of system inertia due to the increasing penetration of wind energy, and other renewable energy systems, and the retirements of conventional power plants. On the other hand, by properly operating wind farms, wind generation may be capable of enhancing grid stability and ensuring continued security of power supply. In this doctoral thesis, new control approaches for designing wind farm optimization-based control strategies are proposed to improve the participation of wind farms in grid support, specially in primary frequency support., Hoy en día, existe una significativa preocupación entre los Operadores de Sistemas de Transmisión sobre la cresciente penetración de le energía eólica y la tendiente eliminación de las centrales eléctricas convencionales que implica la disminución de la inercia del sistema eléctrico. Operando adecuadamente los parques eólicos, la generación eólica puede mejorar la estabilidad de la red eléctrica y garantizar la seguridad y un continuo suministro de energía. Esta tesis doctoral propone nuevas estrategias para diseñar controladores basados en optimización dinámica para parques eólicos y mejorar la participación de los parques eólicos en el soporte de la red eléctrica. En primer lugar, esta tesis doctoral presenta los enfoques clásicos para el control de parques y turbinas eólicas y cómo los conceptos de control existentes pueden ser explotados para hacer frente a los nuevos desafíos que se esperan de los parques eólicos. Esta tesis doctoral asigna un interés especial a cómo formular la función objetivo de que la reserva de potencia sea maximizada, para ayudar por el suporte de frequencia, y al mismo tiempo seguir la potencia demandada por la red. Sin embargo, el impacto de la estela de viento generada por una turbina sobre otras turbinas necesita ser minimizado para mejorar la reserva de potencia. Por lo tanto, a lo largo de esta tesis se proponen estrategias de control centralizado para parques eólicos enfocadas en distribuir óptimamente la energía entre las turbinas para que el impacto negativo de la estela en la reserva de potencia total se reduzca. Se discuten dos técnicas de control para proporcionar los objetivos de control mencionados anteriormente. Un algoritmo de control óptimo para encontrar la mejor distribución de potencia entre las turbinas en el parque mientras se resuelve un problema iterativo de programación lineal. En segundo lugar, se utiliza la técnica de control predictivo basada en modelo para resolver un problema de control multi-objetivo que tam, Postprint (published version)

Wind energy is one of the most promising sources of renewable energy and has experienced a very rapid growth in the last 15 years. In order to maintain and sustain this growth wind energy needs to be competitive with the other sources of energy, especially with fossil fuels. A major factor keeping the competitiveness of wind energy is the sustained investment made on research and innovation. Active wake wind farm control is part of the research effort that aims to reduce the cost of producing wind energy; these new control strategies have the goal of reducing wind turbine wakes interactions in order to increase the power production and to minimize loads and fatigue. Literature shows good potential for these control strategies; however most of the studies have been made in a virtual environment through the use of computer simulations and as a consequence there is a lack of real field measurement data analysis that could confirm the potential of these strategies. The French National Research Agency SMARTEOLE project implemented two wind farm control strategies in an operating wind farm called La Sole du Moulin Vieux and owned by Engie Green in order to assess the effects that the applied innovative wind farm control strategies have on the blade loads, particularly in terms of blade fatigue. The two wind farm control strategies applied are the curtailment strategy and the yaw-control strategy. It needs to be noted that due to the high sensitivity of loads and fatigue to inflow conditions and due to the limited amount of data available the results observed should be considered as general trends very specific for this particular wind turbine set up and location. In the curtailment strategy the upstream turbine SMV6 loads decreased up to 33% (at 9 m/s) in the flapwise direction and up to 67% (at 8 m/s) in the edgewise direction. Fatigue loads decreased up to 22% (at 9 m/s) in the flapwise direction and up to 2% (at 9 m/s) in the edgewise direction. In the do, European Wind Energy Masters (EWEM) | Rotor Design Track

Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm., Team Jan-Willem van Wingerden

In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control inputs to influence the overall performance by taking wake interactions of the wind turbines into account. A constrained optimization problem is formulated to maximize the total power production of a given wind farm. An adjoint approach as an efficient tool is utilized to compute the gradient for such a large-scale system. The computed gradient is then modified to deal with the defined final set and practical constraints on the wind turbine control inputs. The performance of the wind farm controller is examined for a more realistic test case, a layout of a 2 x 3 wind farm with dynamical changes in wind direction. The effectiveness of the proposed approach is studied through simulations., Team Jan-Willem van Wingerden

Wind turbine wake redirection is a promising concept for wind farm control to increase the total power of a wind farm. Further, the concept aims to avoid partial wake overlap on a downwind wind turbine and hence aims to decrease structural loads. Controller for wake redirection need to account for model uncertainties due to the complexity of wake dynamics. Therefore, this work focuses first on modeling a wind farm using an uncertain plant description and second on the design of a robust H∞ controller for closed-loop wake redirection by applying standard robust modeling and control techniques on a wind farm. The wake center position is estimated and fed back to a controller which uses the yaw actuator to redirect the wake. For several inflow conditions, step simulations are conducted and system identifications are performed to obtain multiple plant models. This set of models is used to derive a nominal plant and an uncertainty set. Both the nominal model and the uncertainty set define the uncertain plant model. The robust controller is then designed showing promising results in a medium-fidelity CFD simulation model with time-varying inflow conditions., Team Jan-Willem van Wingerden

Renewable energy is becoming more and more important in today's society. Wind energy plays an important role in the production of renewable energy. Due to economic advantages, wind turbines are often sited close together, creating wind farms. As a result, the wind turbines in the farm become interconnected due to the wakes of the turbines. In conventional wind farm control, this interconnection is ignored, and wind turbines are operated at their own local optimum. This control strategy is referred to as greedy con- trol. In the wake of a turbine, the wind speed is reduced. As a result, the power production of a wind turbine that is situated in a wake is also significantly lower. The greedy control strategy may therefore not be optimal for wind farm control, which subsequently has been receiving an increasing amount of attention recently. Most research on wind farm control focusses on steady- state optimization, i.e., finding the optimal steady-state given certain con- stant conditions. Since wind flow is always subject to change, the potential gain of this procedure is limited. In this thesis, a closed-loop dynamic wind farm controller is presented us- ing a Model Predictive Control (MPC) framework. The control objective is to maximize the power production of the wind farm, resulting in an eco- nomic MPC problem. As the optimal input is time-dependent, the number of control inputs and states involved increase as the prediction horizon of the MPC problem increases, resulting in a more complex problem than the steady-state optimization problem. As a model, WFSim is used, which is based on 2-dimensional Navier-Stokes equations. This results in a high-dimensional problem from which the opti- mum is unknown. The adjoint method is applied to determine the gradient of the objective function in a time-efficient manner. The controller developed in this thesis increases the power production of a wind farm with respect to the conventional greedy control stategy, and is able to, Mechanical, Maritime and Materials Engineering, Delft Center for Systems and Control (DCSC), Systems and Control

Wind turbine wake redirection is a promising concept for wind farm control to increase the total power of a wind farm. Further, the concept aims to avoid partial wake overlap on a downwind wind turbine and hence aims to decrease structural loads. Controller for wake redirection need to account for model uncertainties due to the complexity of wake dynamics. Therefore, this work focuses first on modeling a wind farm using an uncertain plant description and second on the design of a robust H∞ controller for closed-loop wake redirection by applying standard robust modeling and control techniques on a wind farm. The wake center position is estimated and fed back to a controller which uses the yaw actuator to redirect the wake. For several inflow conditions, step simulations are conducted and system identifications are performed to obtain multiple plant models. This set of models is used to derive a nominal plant and an uncertainty set. Both the nominal model and the uncertainty set define the uncertain plant model. The robust controller is then designed showing promising results in a medium-fidelity CFD simulation model with time-varying inflow conditions., Team Jan-Willem van Wingerden

Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm., Team Jan-Willem van Wingerden

Renewable energy is becoming more and more important in today's society. Wind energy plays an important role in the production of renewable energy. Due to economic advantages, wind turbines are often sited close together, creating wind farms. As a result, the wind turbines in the farm become interconnected due to the wakes of the turbines. In conventional wind farm control, this interconnection is ignored, and wind turbines are operated at their own local optimum. This control strategy is referred to as greedy con- trol. In the wake of a turbine, the wind speed is reduced. As a result, the power production of a wind turbine that is situated in a wake is also significantly lower. The greedy control strategy may therefore not be optimal for wind farm control, which subsequently has been receiving an increasing amount of attention recently. Most research on wind farm control focusses on steady- state optimization, i.e., finding the optimal steady-state given certain con- stant conditions. Since wind flow is always subject to change, the potential gain of this procedure is limited. In this thesis, a closed-loop dynamic wind farm controller is presented us- ing a Model Predictive Control (MPC) framework. The control objective is to maximize the power production of the wind farm, resulting in an eco- nomic MPC problem. As the optimal input is time-dependent, the number of control inputs and states involved increase as the prediction horizon of the MPC problem increases, resulting in a more complex problem than the steady-state optimization problem. As a model, WFSim is used, which is based on 2-dimensional Navier-Stokes equations. This results in a high-dimensional problem from which the opti- mum is unknown. The adjoint method is applied to determine the gradient of the objective function in a time-efficient manner. The controller developed in this thesis increases the power production of a wind farm with respect to the conventional greedy control stategy, and is able to, Mechanical, Maritime and Materials Engineering, Delft Center for Systems and Control (DCSC), Systems and Control

In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control inputs to influence the overall performance by taking wake interactions of the wind turbines into account. A constrained optimization problem is formulated to maximize the total power production of a given wind farm. An adjoint approach as an efficient tool is utilized to compute the gradient for such a large-scale system. The computed gradient is then modified to deal with the defined final set and practical constraints on the wind turbine control inputs. The performance of the wind farm controller is examined for a more realistic test case, a layout of a 2 x 3 wind farm with dynamical changes in wind direction. The effectiveness of the proposed approach is studied through simulations., Team Jan-Willem van Wingerden

This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger wind turbine load reduction, compared to the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS.

Optimal flow control problems are important for applications in science and engineering. Solving such problems usually requires the solution of a large linear generalized saddle-point system. This linear system is sparse and highly indefinite. In order to solve such systems using Krylov subspace methods, efficient preconditioners are necessary to enhance their robustness and accelerate the convergence. Standard block preconditioning techniques for the generalized saddle-point systems require an efficient approximation of the Schur complement. This is a big challenge since the Schur complement is large and dense, and therefore computationally expensive to approximate. For some problems, it is even impossible to approximate the Schur complement efficiently. In this dissertation, we propose a new class of preconditioners for optimal flow control problems using multilevel sequentially semiseparable (MSSS) matrix computations. In contrast to standard block preconditioners, MSSS preconditioners do not approximate the Schur complement of the generalized saddle-point system but compute an approximate factorization of the global block system in linear computational complexity. This is a big advantage over block preconditioners. The key to this global factorization is that the Schur complements in this factorization usually have low off-diagonal rank. Therefore, these Schur complements can be approximated by matrices with a low rank off-diagonal structure. For this, MSSS matrix computations are very well suited. Theoretical analysis shows that MSSS preconditioners yield a spectrum of the preconditioned system matrix that is contained in a circle centered at $(1, 0)$. This radius can be controlled arbitrarily by properly choosing a parameter in the MSSS preconditioner computations. This in turn implies that the convergence of MSSS preconditioned systems can be independent of the mesh size and regularization parameter for PDE-constrained optimization problems while for computat, Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

Optimal flow control problems are important for applications in science and engineering. Solving such problems usually requires the solution of a large linear generalized saddle-point system. This linear system is sparse and highly indefinite. In order to solve such systems using Krylov subspace methods, efficient preconditioners are necessary to enhance their robustness and accelerate the convergence. Standard block preconditioning techniques for the generalized saddle-point systems require an efficient approximation of the Schur complement. This is a big challenge since the Schur complement is large and dense, and therefore computationally expensive to approximate. For some problems, it is even impossible to approximate the Schur complement efficiently. In this dissertation, we propose a new class of preconditioners for optimal flow control problems using multilevel sequentially semiseparable (MSSS) matrix computations. In contrast to standard block preconditioners, MSSS preconditioners do not approximate the Schur complement of the generalized saddle-point system but compute an approximate factorization of the global block system in linear computational complexity. This is a big advantage over block preconditioners. The key to this global factorization is that the Schur complements in this factorization usually have low off-diagonal rank. Therefore, these Schur complements can be approximated by matrices with a low rank off-diagonal structure. For this, MSSS matrix computations are very well suited. Theoretical analysis shows that MSSS preconditioners yield a spectrum of the preconditioned system matrix that is contained in a circle centered at $(1, 0)$. This radius can be controlled arbitrarily by properly choosing a parameter in the MSSS preconditioner computations. This in turn implies that the convergence of MSSS preconditioned systems can be independent of the mesh size and regularization parameter for PDE-constrained optimization problems while for computat, Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.

This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large-scale wind farm control.

In a dispatchable wind power plant, turbines are free to continuously vary their power production as long as the sum of their productions meets the total power demand. Previous research has shown that this freedom can be used to reduce structural loads by allowing turbines in the plant to coordinate their power. This paper explains the mechanisms that make power coordination useful for reducing structural loads on the turbine tower and the low speed shaft. In addition, it assesses the benefits of coordination at different operating points.

The problem of load mitigation in wind turbines located in wind farms is addressed. The benefits of letting turbines communicate with and account for their neighbors are explored. First, the idea of exploiting previewed wind speed measurements is examined. The problem is formulated as an H 2 model matching optimization. The influence of preview length on the performance is analyzed and simulation results are presented. Then, the possibility of cooperation between turbines is studied within a distributed feedforward control scheme. The problem is formulated as a decentralized model matching optimization and several theoretical challenges associated with it are outlined. An approximate frequency domain solution and preliminary simulation results are presented.

Wind turbines operating in wind farms are coupled by the wind flow. This coupling results in increased turbulence levels for downwind turbines, and consequently higher maintenance costs. In this paper, we consider a scenario where a wind farm is asked to produce less than maximum power. The objective is to minimize fatigue loads on the turbines, while maintaining the desired power production. We show how this can be achieved by coordinating neighboring turbines. The result is a control structure where each turbine only need to communicate with a limited set of neighboring turbines. Moreover, the synthesis of the controllers is also distributed.