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An example of a capacity curve showing how the power output, CP, and Ct change with wind speed. Data from Kelley²³
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The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structura...
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Enhancing wake recovery behind wind turbines has the potential to significantly improve the power production and efficiency of large wind farms. Rather than investigating turbine control strategies, floater motion or global turbulent quantities such as turbulence intensity, this work aims to study wake stability and recovery through a focus on the...
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The helix approach is a new individual pitch control method to mitigate wake effects of wind turbines. Its name is derived from the helical shape of the wake caused by a rotating radial force exerted by the turbine. While its potential to increase power production has been shown in previous studies, the physics of the helical wake are not well unde...
Citations
... Traditional control strategies aim to maximize the power generation of individual wind turbines, often disregarding that of the entire wind farm (Deljouyi et al., 2021;Park and Law, 2016). In contrast, advanced control strategies strive to increase the total power generated by wind farms or extend the service life of wind turbines by mitigating the wake effect through controlling the pitch, torque, and yaw (Houck, 2021;Nash et al., 2021;Yang et al., 2021). The yaw control strategy involves redirecting the wake from upstream wind turbines to downstream wind turbines through active yaw-misalignment (Mendez Reyes et al., 2019), thereby simultaneously increasing the power generation of downstream wind turbines and that of the entire wind farm (Doekemeijer et al., 2021). ...
Yaw optimization control is recognized as the most effective active wake control strategy for enhancing the overall power generation of wind farms. However, the potential adverse effects of yaw optimization control on the fatigue life of wind turbines remain unclear. This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts. Initially, the yaw misalignment of the wind turbines was optimized using the open-source FLORIS package to maximize wind farm power generation. Subsequently, the time-varying load of the wind turbines was obtained through the OpenFAST software, using the optimal yaw misalignment, wind speed, and turbulence intensity at the hub height as inputs. The fatigue life of the wind turbines was then calculated by integrating the Schmidt-Neuper engineering stress algorithm, rain flow counting method, and Miner-Palmgren fatigue damage accumulation theory. Finally, a case study of the Horns Rev I large-scale offshore wind farm, comprising 80 wind turbines, revealed that yaw optimization control could significantly enhance the annual power generation of the wind farm by approximately 1.818%. However, optimization comes at the cost of reducing the fatigue life of wind turbine tower bolts by approximately 3-9 years.
... Using methods such as wake steering [1] and/or derating [2,3], as well as their dynamic applications [4,5], wake interactions between turbines can be mitigated, extending the life of the turbines while increasing the power output. Such benefits are achieved via optimised distribution of set-points among the turbines [6,7,8]. The core of these optimisations is a low-fidelity wind farm simulator, such as PyWake [9], FLORIS [10], and many more [11], where the performance of a wind farm configuration can be quickly estimated in the optimisation loop using either gradient-free or gradient-based methods [12]. ...
In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.
... Both wake steering and induction control have demonstrated promising outcomes in terms of controlling wind farm power output [1,2] as well as potentially mitigating turbine structural loads [3]. While wake steering has become the dominantly studied strategy among the two due to its benefits on farm power production, several investigations advocate for the concurrent implementation of both yaw and induction control strategies [4,5,6]. ...
... This intentional derating may be employed to meet grid demands [21], pursue enhanced power generation in wind farms through wake control [1], or achieve structural load reduction [22,3]. Various derating methods, involving adjustments in pitch and rotor speed, have been discussed in the literature [23,24,2]. In this study, we will primarily explore the thrust-minimizing derating trajectory, min-C T , which aims to minimize the thrust coefficient for a target power coefficient, C P,target , for a fixed yaw angle, γ target .: min θp,λ C T (θ p , λ, γ) ...
Wind turbine control via concurrent yaw misalignment and axial induction control has demonstrated potential for improving wind farm power output and mitigating structural loads. However, the complex aerodynamic interplay between these two effects requires deeper investigation. This study presents a modified blade element momentum (BEM) model that matches rotor-averaged quantities to an actuator disk model of yawed rotor induction, enabling analysis of joint yaw-induction control using realistic turbine control inputs. The BEM approach reveals that common torque control strategies such as K − Ω ² exhibit sub-optimal performance under yawed conditions. Notably, the power-yaw and thrust-yaw sensitivities vary significantly depending on the chosen control strategy, contrary to common modeling assumptions. In the context of wind farm control, employing induction control which minimizes the thrust coefficient proves most effective at reducing wake strength for a given power output across all yaw angles. Results indicate that while yaw control deflects wakes effectively, induction control more directly influences wake velocity magnitude, underscoring their complementary effects. This study advances a fundamental understanding of turbine aerodynamic responses in yawed operation and sets the stage for modeling joint yaw and induction control in wind farms.
... The wake of a wind turbine presents complications for nearby turbines, depending on the atmospheric conditions, turbine characteristics, and turbine siting. The nascent field of wind farm flow control seeks to reduce the deleterious effects of the wake momentum deficit by leveraging the turbine as a flow actuator though the intelligent scheduling of either the blade pitch, rotor speed, or nacelle yaw [1,2]. Wind farm flow control approaches fall into three categories: wake reduction (i.e., reducing the energy extraction of upstream turbines to increase the net power in the wind farm; this is commonly known as turbine derating), wake steering (i.e., deflecting the wakes of upstream turbines around downstream turbines), and wake mixing (i.e., actuating the wake periodically to increase mixing with the surrounding ambient flow). ...
... where subscript b denotes the blade number, θ 0 is the nominal pitch command of the controller in degrees, ψ b is the blade azimuth in radians from the top-dead center, and where the quantities in the column vector are given by Equations (2)- (4) θ axi (t) = A axi sin(ω e t) (2) θ tilt (t) = A tilt sin(ω e t) ...
A reduction in wake effects in large wind farms through wake-aware control has considerable potential to improve farm efficiency. This work examines the success of several emerging, empirically derived control methods that modify wind turbine wakes (i.e., the pulse method, helix method, and related methods) based on Strouhal numbers on the O(0.3). Drawing on previous work in the literature for jet and bluff-body flows, the analyses leverage the normal-mode representation of wake instabilities to characterize the large-scale wake meandering observed in actuated wakes. Idealized large-eddy simulations (LES) using an actuator-line representation of the turbine blades indicate that the n=0 and ±1 modes, which correspond to the pulse and helix forcing strategies, respectively, have faster initial growth rates than higher-order modes, suggesting these lower-order modes are more appropriate for wake control. Exciting these lower-order modes with periodic pitching of the blades produces increased modal growth, higher entrainment into the wake, and faster wake recovery. Modal energy gain and the entrainment rate both increase with streamwise distance from the rotor until the intermediate wake. This suggests that the wake meandering dynamics, which share close ties with the relatively well-characterized meandering dynamics in jet and bluff-body flows, are an essential component of the success of wind turbine wake control methods. A spatial linear stability analysis is also performed on the wake flows and yields insights on the modal evolution. In the context of the normal-mode representation of wake instabilities, these findings represent the first literature examining the characteristics of the wake meandering stemming from intentional Strouhal-timed wake actuation, and they help guide the ongoing work to understand the fluid-dynamic origins of the success of the pulse, helix, and related methods.
... ratio. 5,6 While AIC has proven capable of providing grid services [7][8][9] and decreasing structural loading, 10,11 applying it for maximizing power has seen less convincing results. [12][13][14][15] The second category is called wake steering and has been shown numerous times to be capable of increasing the power output of wind turbine arrays. ...
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%.
... Thus, the concept of control in the wind-energy science is shifting from the turbine-level greedy approach to a collective farm-level one to guarantee more efficient wind farms with low operation costs. From a fluidmechanics viewpoint, this translates into extensive research at the crossroads of turbine aerodynamics [7-9] , wind-farm flow modeling [10][11][12][13] , and collective wind-farm flow control (CWFC) [14][15][16][17][18][19] , with the last one being the most challenging task owing to the inherent complexities of the system under control. CWFC seeks methodologies for the optimization of the output power of the farm and the reduction and/or more evenly distribution of loads on the elements of turbines. ...
Wind-farm flow control stands at the forefront of grand challenges in wind-energy science. The central issue is that current algorithms are based on simplified models and, thus, fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions. Reinforcement learning (RL), as a subset of machine learning, has demonstrated its effectiveness in solving high-dimensional problems in various domains, and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control. This review has two main objectives. Firstly, it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods. By examining the latest research in this area, the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques. Secondly, it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL. By highlighting these challenges, the review aims to identify areas requiring further exploration and potential opportunities for future research.
... Although it can be achieved through 735 various methods, this section focuses on the the most popular method found in the literature, the use of static yaw misalignment of upstream turbines. Similar to the objectives of yaw control, in wake steering control, the goal is to balance yawing frequently enough to maintain power maximisation while avoiding overuse of the yawing components (Houck, 2022). Contrary to the objectives of yaw control, however, upstream turbines are operated with an intentional yaw misalignment to redirect their wakes away from downstream turbines, therefore mitigating potentially substantial power losses caused by wake effects (Howland 740 et al., 2019). ...
Wind direction variability significantly affects the performance and life-time of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development, and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LES), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high and low-fidelity models.
... [29][30][31] The purpose of this study is to investigate how to reduce energy loss of offshore wind farms subject to wake effects during preventive maintenance. Wake effects have been seriously considered in location selection for wind farms, 32 wind farm layout optimization, 33 reduction of fatigue loads on wind turbines, 34 and estimation of available power during curtailment. 35 The optimization of maintenance schedule has taken wake effects into account initially by Zhang et al. 36 The coupling between maintenance and wake effects was investigated using randomly generated stochastic sampling wind speed and direction by Ge et al 37 and Yin et al. 38 However, the reduction in power generation caused by wake effects during maintenance has not been thoroughly studied in practice. ...
The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.
... Given this premise, it is evident that the analysis of wind turbine wakes is a topic that has attracted an extremely vast amount of literature, dealing with several aspects. Some of the most important are wind tunnel analysis [17][18][19], wind farm control [20,21], numerical simulations [22][23][24][25][26], and so on. ...
Wakes between neighboring wind turbines are a significant source of energy loss in wind farm operations. Extensive research has been conducted to analyze and understand wind turbine wakes, ranging from aerodynamic descriptions to advanced control strategies. However, there is a relatively overlooked research area focused on characterizing real-world wind farm operations under wake conditions using Supervisory Control And Data Acquisition (SCADA) parameters. This study aims to address this gap by presenting a detailed discussion based on SCADA data analysis from a real-world test case. The analysis focuses on two selected wind turbines within an onshore wind farm operating under wake conditions. Operation curves and data-driven methods are utilized to describe the turbines’ performance. Particularly, the analysis of the operation curves reveals that a wind turbine operating within a wake experiences reduced power production not only due to the velocity deficit but also due to increased turbulence intensity caused by the wake. This effect is particularly prominent during partial load operation when the rotational speed saturates. The turbulence intensity, manifested in the variability of rotational speed and blade pitch, emerges as the crucial factor determining the extent of wake-induced power loss. The findings indicate that turbulence intensity is strongly correlated with the proximity of the wind direction to the center of the wake sector. However, it is important to consider that these two factors may convey slightly different information, possibly influenced by terrain effects. Therefore, both turbulence intensity and wind direction should be taken into account to accurately describe the behavior of wind turbines operating within wakes.
... In reality, turbines are subject to wake-effect interference, which causes shrinkage of the delivered power from downstream turbines, shorter lifetime and ultimately increased energy cost. Therefore, it is essential to develop techniques to efficiently managing the wakes of larger wind farms, called the wake effect [28,29]. Depending on the position of the upstream turbines, different wind speeds and directions can affect downstream turbines. ...
The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind farms. Various types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power system. In this study, dynamic simulation of equivalent models of a sample wind farm, including single-turbine representation, multiple-turbine representation, quasi-multiple-turbine representation and full-turbine representation models, are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT software. The developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate performance. The wake effects between wind turbines for the full-turbine representation and multiple-turbine representation models have been considered using the Jensen method. The developed model improves the extraction power of the turbine according to the layout of the wind farm. The accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm, including active and reactive powers, voltage and instantaneous flicker intensity. The study was carried out on a sample wind farm, which included 39 wind turbines. The simulation results confirm that the computational loads of the single-turbine representation (STR), the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39, 1/8 and 1/8 times the full-turbine representation model, respectively. On the other hand, the error of active power (voltage) with respect to the full-turbine representation model is 74.59% (1.31%), 43.29% (0.31%) and 7.19% (0.11%) for the STR, the multiple-turbine representation and the quasi-multiple representation, respectively.
