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Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems. [ABSTRACT FROM AUTHOR]

Abstract Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems.

Abstract 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%.

The wake effect will cause wind farm power loss. Wake redirection is a wind farm control strategy to reduce wake losses and increase total power output. In the previous research, analytical wake models with steady-wind-condition assumptions were always employed to optimize the yaw angle of wind turbines, in which wind direction change and yaw process were not considered. Therefore, this study proposes a dynamic yaw control methodology for wind farms to balance the energy gain and the number of yaw actions. It was found that when the dead band is 10°, the past time is 15 min, and the wind direction bins step is 8°, the number of yaws of the front row unit is only 97.2% of that under the conventional control, and the total power generation is increased by 3.41%. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

In this paper, a supervisory control concept for wind farms is proposed based on the neighboring wind turbines control functions in localized areas for power optimization considering wake effects. The flow control in wind farms to maximize power production is a challenging problem due to its time-varying nonlinear wake dynamics. Hence, we develop a method that authorizes coordination in a wind farm for a squarely payoff-based scenario where the turbines have access only to measurements from their neighbors via repeated interactions. Therefore, in order to maximize output power in a wind farm, an Adaptive Learning Game Theory (ALGT) method is introduced. This control scheme provides an interaction framework that constructs a series of common control functions. Here, in every iteration, each turbine chooses an independent decision according to a localized control law. The control objective of wind turbine i determines how each turbine adjusts a decision at each iteration by processing available information. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a data-driven approach to wind farm control, offering an alternative to the FLORIS wind farm simulator. Our method estimates the power of a wind farm and determines the optimal yaw angle to maximize power generation. Initially, we develop a power estimation neural network using data from FLORIS for power estimation and validate its accuracy and reliability. Subsequently, this power estimation neural network is employed to determine the optimal yaw angle for maximum power production. The efficacy of this yaw decision neural network is verified through various performance metrics. We then present dynamic simulations by integrating the yaw decision neural network, constructed through our data-driven approach, with a dynamic wind farm simulator. We believe this addresses the limitations of FLORIS, a steady-state simulator. Our results demonstrate the effectiveness of the proposed yaw decision neural network in dynamic environments, underscoring the potential of a data-driven approach to overcome the challenges posed by the steady-state wind farm simulator. This study offers innovative solutions for the efficient control and optimization of wind farm.

This paper presents a data-driven approach to maximize the power of a wind farm by developing a dynamic mode decomposition with input and output for reduced order model (DMDior)-based reduced order model (ROM) for model predictive control (MPC). The main goal of this research is to efficiently model and manage the complex flow field within a wind farm to enhance power production. We leveraged DMDior to transform extensive high-dimensional flow data into an accurate yet simplified ROM, which successfully represents the essential dynamic features of wind flow, including the critical interactions between turbines and their adaptive response to environmental changes. Based on this ROM, the MPC framework was carefully designed. MPC uses this model to dynamically adjust the yaw angle of a wind turbine to optimally match changing wind patterns to maximize power output. The system also incorporates an adaptive Kalman filter designed for the state estimation in MPC applications. This estimation is critical to the effective execution of the MPC in each iteration. This ensures that the MPC operates based on the most up-to-date and accurate representation of the wind farm’s state, improving the overall reliability and efficiency of the control strategy. This approach demonstrates a practical and effective way to increase the power output of a wind farm, with experimental results indicating a power increase of about 4.72%.

This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine's optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine's internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Summary Recently, dynamic induction control is gaining the interest of the wind energy community as a promising strategy to increase the overall wind farm power production. Such a technique is based on a dynamic variation of the upstream rotor thrust, generated through a suitable blade pitch motion, to promote a faster wake recovery. Notwithstanding some promising results already published, the knowledge of the physical mechanism, connecting dynamic induction to the increased in‐wake velocity, was not yet exploited to enhance control effectiveness. This paper, through a computational fluid dynamics procedure based on large eddy simulations coupled with actuator line models, provides a description of the working principles of this control from a fluid dynamics standpoint. The analyses show that the faster recovery is strictly connected to the ability of the blade tip vortices to roll up and sucking energy from the outer flow. Exploiting such knowledge, a novel control strategy, which improves the vortex roll up mechanism, is proposed and analyzed. The new control proved more effective than standard techniques especially for very low turbine spacing.

The impact of rotor setting and relative arrangement on the individual and overall power performance and aerodynamics of double rotor vertical axis wind turbine (VAWT) arrays is investigated. Eight rotor settings are considered: two relative rotational directions (co-rotating, CO, and counter-rotating, CN), two relative positionings (downstream turbine positioned in the leeward, LW, and windward, WW, of the upstream rotor), and two phase lags (Δθ= 0° and 180°). For each of the eight rotor settings, 63 different relative arrangements are considered resulting in 504 unique cases. The arrangements are considered within 1.25d ≤ R ≤ 10d (d = rotor diameter) and 0° ≤Φ≤ 90°, where R and Φ are relative distance and angle of the rotors, respectively. Unsteady Reynolds-Averaged Navier–Stokes (URANS) CFD simulations, validated with experimental data, are employed. The results show that the power performance of the array is significantly influenced by the relative rotational direction and positioning, ∼8% in power coefficient (CP), while it is marginally dependent on relative phase lag. The different performance of the studied arrays is because of different parts of the downstream turbine revolution being affected by the wake of the upstream turbine and dissimilar strength/width of the shear layer created in the two rotors’ wake overlap. The preferred rotational direction for WW arrays is co-rotating while for LW arrays counter-rotating is favored. For the same arrangement, counter-rotating turbines with LW relative positioning have the highest CPdue to their downstream turbine blade moving along the flow direction in the wake overlap region resulting in little energy dissipation and weak shear layer. In contrast, counter-rotating arrays with WW relative positioning have the lowest CP, because the downstream turbine blade moves against the flow in the wake overlap region, resulting in extensive velocity deficit and a thick, strong shear layer.

Green hydrogen is likely to play an important role in meeting the net-zero targets of countries around the globe. One potential option for green hydrogen production is to run electrolysers directly from offshore wind turbines, with no grid connection and hence no expensive cabling to shore. In this work, an innovative proof of concept of a wind farm control methodology designed to reduce variability in wind farm active power output is presented. Smoothing the power supplied by the wind farm to the battery reduces the size and number of battery charge cycles and helps to increase battery lifetime. This work quantifies the impact of the wind farm control method on battery lifetime for wind farms of 1, 4, 9 and 16 wind turbines using suitable wind farm, battery and electrolyser models. The work presented shows that wind farm control for smoothing wind farm power output could play a critical role in reducing the levelised cost of green hydrogen produced from wind farms with no grid connection by reducing the damaging load cycles on batteries in the system. Hence, this work paves the way for the design and testing of a full implementation of the wind farm controller. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of three wind farm simulators, examining the influence of wake on the local wind speed and power output for downstream turbines using experimental data. The study features experiments in three distinct scenarios, evaluating differences among the simulators by calculating the local wind speed and power for each. Each simulator employs a unique wake model, which substantially affects the local wind speed experienced by downstream turbines. Furthermore, the experiment involves adjusting parameter values for each simulator to assess their respective impacts on wind farm performance. The findings of this research are expected to play an important role in investigations related to power optimization and wake effects in the wind farm control. [ABSTRACT FROM AUTHOR]

Abstract As wind farms become larger, there is scope for improved operation via wind farm control. Further development of wind farm control would be facilitated by more flexible operation of wind farms and so by more flexible operation of wind turbines. A novel approach to wind farm control is proposed that provides full flexibility of both. It consists of a wind farm controller architecture and an interface to individual turbines. The design of a specific realisation of the interface, the Power Adjusting Controller, is presented that requires little information on the turbine dynamics or controller and does not compromise the operation of the wind turbine controller or the turbine's safety. Results from a DNV Bladed simulation of a 5MW wind turbine are presented to illustrate the behaviour of the Power Adjusting Controller and to confirm that it meets the requirements to enable fully flexible operation of wind turbines and, so of wind farms.

Summary: Recently, dynamic induction control is gaining the interest of the wind energy community as a promising strategy to increase the overall wind farm power production. Such a technique is based on a dynamic variation of the upstream rotor thrust, generated through a suitable blade pitch motion, to promote a faster wake recovery. Notwithstanding some promising results already published, the knowledge of the physical mechanism, connecting dynamic induction to the increased in‐wake velocity, was not yet exploited to enhance control effectiveness. This paper, through a computational fluid dynamics procedure based on large eddy simulations coupled with actuator line models, provides a description of the working principles of this control from a fluid dynamics standpoint. The analyses show that the faster recovery is strictly connected to the ability of the blade tip vortices to roll up and sucking energy from the outer flow. Exploiting such knowledge, a novel control strategy, which improves the vortex roll up mechanism, is proposed and analyzed. The new control proved more effective than standard techniques especially for very low turbine spacing. [ABSTRACT FROM AUTHOR]

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. • Wind farms can provide ancillary services while considering instantaneous loads. • A three-pronged approach enhances power reliability and improves structure safety. • Less is more: derating allows extended service life by reducing structure damage. • The profitability of wind farms can be increased via power tracking strategies. [ABSTRACT FROM AUTHOR]

Wake models play a fundamental role in predicting loads and power generated by wind farms. Using them, it is possible to assess how wakes develop and interact with each other and what are the effects generated in the context of a real operating farm. In this paper, a Gaussian Wake Model implemented in OpenFAST is validated against experimental data gathered in wind tunnel GVPM @ Politecnico di Milano in the context of the European CL-Windcon project. The software used to reproduce the mechanical dynamics of the G1 wind turbines is OpenFAST. It is coupled with FLORIS, a NREL's software based on the Gaussian Wake Model, that allows to simulate the wake and partial wake conditions. The rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. However, to properly use OpenFAST and the coupled GWM, eight parameters have to be estimated. Thus, a Least Squares Minimization procedure is performed using the available experimental data. [ABSTRACT FROM AUTHOR]

• A method of wind farm power optimization is developed using system identification. • A fast convergent gradient-ascend optimization procedure is proposed using large initial step size based on process knowledge and a variable step size scheme. • The method is verified using the well-known wind farm model FLORIS of National Renewable Energy Lab of United States. The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool. [ABSTRACT FROM AUTHOR]

The wake effect, which is caused by the upstream turbines in a wind farm, adversely affects the efficiency of downstream turbines, leading to reduced energy generation and increased turbine fatigue loading. To mitigate this effect, a real-time wind farm control technique, i.e., wake redirection control (WRC), employing teaching learning-based optimization (TLBO) is introduced. This technique redirects the wakes away from the downstream turbines in real time, allowing them to generate more power by sacrificing some of the power generated by the upstream turbines. As a result, the total power generated by the wind farm is maximized. A low-fidelity 20-turbine real-life offshore wind farm is modeled and simulated in FLORISSE_M, the MATLAB version of the FLORIS (FLOw Redirection and Induction in Steady-state). The power produced by the wind farm model is maximized in real time by employing TLBO. The optimization results (i.e., the optimized yaw angles) are validated using the corresponding high-fidelity wind farm model developed in SOWFA (Simulator fOr Wind Farm Applications). Various results are presented to demonstrate that the TLBO-based WRC positively affects the power generated by the wind farm. • The power output of a model of a real-life offshore wind farm in South Korea is optimized. • Wake redirection control (WRC) based on Teaching Learning-based Optimization (TLBO) is proposed. • The proposed WRC is developed and applied in real time to the FLORIS model of the Korean wind farm. • The proposed WRC is validated using a high-fidelity model of the Korean wind farm in SOWFA. • Results demonstrate that the TLBO-based WRC could increase the overall wind farm power output. [ABSTRACT FROM AUTHOR]

This article presents a thorough methodology for the voltage assessment of pilot offshore wind project in Shandong province, China. The results presented in this paper can be considered as the milestone of the offshore wind research in Shandong Province since there is still no grid codes for offshore wind power plant grid connection in the local electrical power grid. It mainly consists of three parts. In the first part, a detailed model of the offshore wind farm created in the DIgSILENT PowerFacotry simulation platform is presented, including wind turbines, power converters, transformers, submarine cables and relevant control schemes. In the second part, nonlinear time‐domain simulations were performed to analyze the wind farm's active power, reactive power, and voltage conditions under different wind scenarios. Based on the simulations results, a dynamic reactive power compensation system was proposed, and the consequence of the reactive power compensation was also demonstrated using nonlinear time‐domain simulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract This article presents a thorough methodology for the voltage assessment of pilot offshore wind project in Shandong province, China. The results presented in this paper can be considered as the milestone of the offshore wind research in Shandong Province since there is still no grid codes for offshore wind power plant grid connection in the local electrical power grid. It mainly consists of three parts. In the first part, a detailed model of the offshore wind farm created in the DIgSILENT PowerFacotry simulation platform is presented, including wind turbines, power converters, transformers, submarine cables and relevant control schemes. In the second part, nonlinear time‐domain simulations were performed to analyze the wind farm's active power, reactive power, and voltage conditions under different wind scenarios. Based on the simulations results, a dynamic reactive power compensation system was proposed, and the consequence of the reactive power compensation was also demonstrated using nonlinear time‐domain simulations.

As wind farms become larger, there is scope for improved operation via wind farm control. Further development of wind farm control would be facilitated by more flexible operation of wind farms and so by more flexible operation of wind turbines. A novel approach to wind farm control is proposed that provides full flexibility of both. It consists of a wind farm controller architecture and an interface to individual turbines. The design of a specific realisation of the interface, the Power Adjusting Controller, is presented that requires little information on the turbine dynamics or controller and does not compromise the operation of the wind turbine controller or the turbine's safety. Results from a DNV Bladed simulation of a 5MW wind turbine are presented to illustrate the behaviour of the Power Adjusting Controller and to confirm that it meets the requirements to enable fully flexible operation of wind turbines and, so of wind farms. [ABSTRACT FROM AUTHOR]

A deep-reinforcement-learning (DRL) based control method to take the advantage of complex wake interactions in a wind farm is developed. Although the wind over a wind farm is changing, steady wind has been assumed in the most conventional methods for wind farm control. Under unsteady wind, the generated power of a wind farm becomes stochastic due to intermittent and fluctuating wind. To tackle the difficulty, a DRL-based method with which the pitch and yaw angles of wind turbines in a wind farm are strategically controlled is developed. Time-histories of the past wind and the predicted future wind are both utilized to identify the relation between the generated power and control. The present neural network is trained and validated using an experimental wind farm. A multi-fan wind tunnel is developed to generate unsteady wind for experiments with miniature wind farms, where the improvement in the generated power by the present DRL-based control method is demonstrated. • The necessity of optimal control under time-varying wind is analyzed. • A DRL-based method for wind farm control under time-varying wind is developed. • A neural-network-based method for prediction of future wind is proposed. • Experiments for a wind farm with miniature turbines in a wind tunnel are conducted. • Improvement of the power production by the proposed control method is demonstrated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Concepts for control of wind farms (WFs) can be clustered into two distinct concepts, namely, wind power plant control (WPPC) and wind farm flow control (WFFC). WPPC 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 WF without consideration of aerodynamic turbine interaction. However, flow phenomena like wake effects can have a large impact on the overall performance of the WF. WFFC considers such aerodynamic phenomena in the WF 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 WF control. [ABSTRACT FROM AUTHOR]

In this article, a deep reinforcement learning (RL)-based control approach with enhanced learning efficiency and effectiveness is proposed to address the wind farm control problem. Specifically, a novel composite experience replay (CER) strategy is designed and embedded in the deep deterministic policy gradient (DDPG) algorithm. CER provides a new sampling scheme that can mine the information of stored transitions in-depth by making a tradeoff between rewards and temporal difference (TD) errors. Modified importance-sampling weights are introduced to the training process of neural networks (NNs) to deal with the distribution mismatching problem induced by CER. Then, our CER-DDPG approach is applied to optimizing the total power production of wind farms. The main challenge of this control problem comes from the strong wake effects among wind turbines and the stochastic features of environments, rendering it intractable for conventional control approaches. A reward regularization process is designed along with the CER-DDPG, which employs an additional NN to handle the bias of rewards caused by the stochastic wind speeds. Tests with a dynamic wind farm simulator (WFSim) show that our method achieves higher rewards with less training costs than conventional deep RL-based control approaches, and it has the ability to increase the total power generation of wind farms with different specifications. [ABSTRACT FROM AUTHOR]

Abstract Wind turbine wakes negatively affect downwind turbines in wind farms reducing their global efficiency. The reduction of wake‐turbine interactions by actuating control on yaw angles and induction factors is an active area of research. In this study, the capability of spanwise‐periodic rows of wind turbines with tilted rotors to reduce negative wake‐turbine interactions is investigated through large‐eddy simulations. It is shown that, by means of rotor tilt, it is possible to replace turbine far wakes with high‐speed streaks where the streamwise velocity exceeds the freestream velocity at hub height. Considering three aligned rows of wind turbines, it is found that the global power extracted from the wind can be increased by tilting rotors of the upwind turbine rows, similarly to what is already known for the case of a single column of aligned turbines. It is further shown that global tilt‐induced power gains can be significantly increased by operating the tilted turbines at higher induction rates. Power gains are further increased for higher ratios of rotor diameters and turbine spacings to the boundary layer height. All these findings are consistent with those of previous studies where streamwise streaks were artificially forced by means of spanwise‐periodic rows of wall‐mounted roughness elements in order to control canonical boundary layers for drag‐reduction applications.

A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the full distributed consensus controller or the central control strategies.

Yaw-based wake steering is pursued as a means to avoid upstream turbine wake influence on downstream turbines. However, the method's effectiveness is likely restricted by rapid wind direction changes that cause wake trajectory oscillations. To address this issue, a thrust-based wake trajectory stabilization method is proposed here. The changes to the cross-stream thrust, resulting from unintentional yaw misalignments, caused due changes in inflow wind direction could be counteracted by changing rotor speed and blade pitch, thus stabilizing wake trajectory oscillations. This paper presents a wind tunnel case study to explore the effect of combining yaw-based wake steering and fast-changing rotor speed control on the cross-stream thrust component and hence on the wake trajectory. Although a feedback controller was not implemented, it is demonstrated that wake trajectory oscillation could be contained using faster actuating mechanisms, such as rotor speed or pitch. Here the wake oscillations due to wind direction changes emulated by sinusoidally yawing the turbine, were restricted by varying turbine rotation speed. Direct thrust measurements as well as detailed Particle Image Velocimetry (PIV) and hotwire wake data show how this combination can successfully suppress the dynamic cross-stream thrust component perturbations and subsequently the wake trajectory oscillation around a desired position. [ABSTRACT FROM AUTHOR]

Operating wind turbines together as a wind farm can be more advantageous and economical. As a result, onshore and offshore wind farms are being built at a rapid pace around the world. Wake effects, which have a negative impact on overall wind farm electricity generation, are one of the key concerns in wind farms. This work concentrates on the maximization of power output from wind farms by ameliorating the wake effect. This work introduces a dynamic wind farm controller that adjusts turbines' yaw angles or axial induction factors following the flow field conditions to maximize the overall power output of the wind farm. This research examines a real-life offshore wind farm in South Korea and the wind farm controller is evaluated in Wind Farm Simulator (WFSim), a control-oriented dynamic wind farm model environment built by Delft University of Technology. The main contribution of this work includes investigating the impact of wind farm control methods on the power production of a wind farm model that simulates a real-life wind farm. [ABSTRACT FROM AUTHOR]

A model-free deep reinforcement learning (DRL) method is proposed in this article to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network (DN)-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-actor mechanism's advantages. Compared with conventional DRL methods, the proposed DN-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts. [ABSTRACT FROM AUTHOR]

Turbines in a wind farm dynamically influence each other through wakes. Therefore trade-offs exist between energy output of upstream turbines and the health of downstream turbines. Using both model-based predictive control (MPC) and machine learning techniques, existing works have explored the energy-fatigue trade-off either in a single turbine or only with few turbines due to issues of scalability and complexity. To address this gap, this paper proposes a multi-agent deep reinforcement learning-based coordinated control for wind farms, called FALCON. FALCON addresses the multi-objective optimization problem of maximizing energy while minimizing fatigue damage by jointly controlling pitch and yaw of all turbines. FALCON achieves scale by using multiple reinforcement learning agents; capturing the global state-space efficiently using an auto-encoder; and pruning the action-space using domain knowledge. FALCON is evaluated through a real-world wind-farm case study with 21 turbines; and performs better than the default baseline PID controller and a learning-based distributed control. • One of the first works using multiagent Reinforcement Learning for wind farm control. • Multi-objective optimization of both power and turbine life. • Novel implementation of RL and auto-encoder for solution scalability. • Performance of the approach is demonstrated on real-world wind farm data. • Results show that 6% more energy with 31 years of extra turbine life can be achieved. [ABSTRACT FROM AUTHOR]

This paper presents a critical review, from a wind farm control perspective, of different methodologies in the open literature that enable wind farms to participate in ancillary service provision. Firstly, it considers the services currently provided in power systems with high levels of wind generation (specifically, Denmark, Ireland, and Great Britain), reviewing current regulatory frameworks and recommendations. Secondly, it reviews the ancillary service markets that wind farms do not currently participate in, considering the barriers to entry and discussing potential solutions using a proper control-enabled framework. Thirdly, it also considers the future perspective for wind farm participation in ancillary service provision, including a review of the body of published academic research on wind farm participation in ancillary service provision. Finally, this review concludes by suggesting where the gaps are in the academic literature, and subsequently suggests future work. Two examples are the disconnect between the mechanical and farm side approaches with power-system-based modelling, and how much wind farm modelling is very-low-fidelity-omittedkey aspects such as wake effects and component fatigue analysis.

In this paper, we propose a power-setpoint based Extremum Seeking Control (ESC) framework for model-free Region-2 controls for maximizing the power capture for turbine and farm operation, without dependency on wind measurement. As a major obstacle for retrofitting wind turbine/farm controls is that only the power setpoint is accessible, the power-setpoint based ESC framework is proposed with a back-calculation anti-windup structure. If increasing the power demand cannot further increase actual power output, the anti-windup structure automatically holds the power demand setpoint. For farm operation, the proposed method is integrated into the Delay-compensated Nested-loop ESC. The proposed method is evaluated by simulations on the SimWindFarm platform for both single-turbine and farm operation scenarios. The results demonstrate the capability of tracking the achievable optimum power for turbine and farm operation, with only reasonable increase of some loads. The proposed method promises an easy-to-implement model-free retrofitting control strategy for enhancing wind energy capture. [ABSTRACT FROM AUTHOR]

Wind farm control (WFC) allows coordinated operation of the wind turbines within a wind power plant (WPP). 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 WPPs and electricity markets necessarily evolve. In such a context, crossfertilization between the two categories of WFC, targeting an integrated approach where applicable, becomes a priority. An initial overview on how to further align wind farm flow control (WFFC) to grid and electricity markets participation is provided, including an analysis of capabilities and prospects. In addition, greater detail is given about an open‐access dataset serving as market showcases for value demonstration of price‐driven operation of WPPs by means of WFFC. [ABSTRACT FROM AUTHOR]

This paper proposed a distributed reactive power coordination and voltage control of offshore wind farm based on the leader-following diffusion algorithm. Designating several wind turbine generators (WTGs) as the leaders to receive the information of voltage at point of common coupling (PCC), the reactive power generations required to minimize the voltage deviation could be computed by these leaders. The required reactive power generations are diffused throughout all WTGs by the diffusion algorithm, resulting in the coordinated operation of WTGs to regulate the PCC voltage. The proposed offshore wind farm controller is based on the hierarchical control strategy, which consists of primary and secondary layers. The primary layer is responsible for the inner current, voltage or power regulations whereas the secondary layer is based on the proposed diffusion algorithm to achieve the coordinated operation among WTGs. The proposed strategy could maintain accurate reactive power sharing among WTGs and regulate the PCC voltage. A comparison study with the conventional consensus-based control is presented to show the effectiveness of the proposed diffusion controller. The comparison results show the better performance of the proposed method in terms of dynamic responses of PCC voltage and reactive power coordination. Simulation scenarios of constant wind speed, variable wind speed, and voltage sag in the utility grid are carried out to evaluate the performance of proposed method. The proposed diffusion control is tested either in a small or large offshore wind farm systems. Effect of communication delay on the performance of proposed diffusion control is also described. An experiment of the small-scale wind farm system is conducted to show the feasibility of proposed diffusion strategy.

Wind turbine wakes negatively affect downwind turbines in wind farms reducing their global efficiency. The reduction of wake‐turbine interactions by actuating control on yaw angles and induction factors is an active area of research. In this study, the capability of spanwise‐periodic rows of wind turbines with tilted rotors to reduce negative wake‐turbine interactions is investigated through large‐eddy simulations. It is shown that, by means of rotor tilt, it is possible to replace turbine far wakes with high‐speed streaks where the streamwise velocity exceeds the freestream velocity at hub height. Considering three aligned rows of wind turbines, it is found that the global power extracted from the wind can be increased by tilting rotors of the upwind turbine rows, similarly to what is already known for the case of a single column of aligned turbines. It is further shown that global tilt‐induced power gains can be significantly increased by operating the tilted turbines at higher induction rates. Power gains are further increased for higher ratios of rotor diameters and turbine spacings to the boundary layer height. All these findings are consistent with those of previous studies where streamwise streaks were artificially forced by means of spanwise‐periodic rows of wall‐mounted roughness elements in order to control canonical boundary layers for drag‐reduction applications. [ABSTRACT FROM AUTHOR]

Operating wind turbines together as a wind farm can be more advantageous and economical. As a result, onshore and offshore wind farms are being built at a rapid pace around the world. Wake effects, which have a negative impact on overall wind farm electricity generation, are one of the key concerns in wind farms. This work concentrates on the maximization of power output from wind farms by ameliorating the wake effect. This work introduces a dynamic wind farm controller that adjusts turbines’ yaw angles or axial induction factors following the flow field conditions to maximize the overall power output of the wind farm. This research examines a real-life offshore wind farm in South Korea and the wind farm controller is evaluated in Wind Farm Simulator (WFSim), a control-oriented dynamic wind farm model environment built by Delft University of Technology. The main contribution of this work includes investigating the impact of wind farm control methods on the power production of a wind farm model that simulates a real-life wind farm.

The power converter is among the most vulnerable wind turbine components. It is thus important to improve its reliability, especially when wind turbines are offshore because they are often exposed to severe weather conditions. A wind turbine is normally regulated using a dedicated controller, coupled with a power converter, but the control strategy proposed here requires a group (or cluster) of turbines to share a controller/converter between several turbines. The shared controller/converter would be placed somewhere more accessible, such as a substation. The potential benefits include improved reliability of each turbine due to the simplification (having removed its vulnerable power converter) and greater energy yield as a result of improved accessibility (which would lead to reduced downtime). The Matlab/Simulink model of Supergen Wind 5 MW exemplar wind turbine is employed to simulate each turbine. In order to simulate a cluster of multiple turbines, each Supergen model is first discretised and, in turn, converted to C to reduce the simulation time, ensuring at the same time that the complexity of each turbine model is not compromised. [ABSTRACT FROM AUTHOR]