Maximization of the annual energy production of wind power plants by optimization of layout and yaw‐based wake control

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw‐based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady‐state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above‐rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.     

Simulation comparison of wake mitigation control strategies for a two‐turbine case

Wind turbines arranged in a wind plant impact each other through their wakes. Wind plant control is an active research field that attempts to improve wind plant performance by coordinating control of individual turbines to take into account these turbine–wake interactions. In this paper, high‐fidelity simulations of a two‐turbine fully waked scenario are used to investigate several wake mitigation strategies, including modification of yaw and tilt angles of an upstream turbine to induce wake skew, as well as repositioning of the downstream turbine. The simulation results are compared through change relative to a baseline operation in terms of overall power capture and loading on the upstream and downstream turbine. Results demonstrated improved power production for all methods. Analysis of control options, including individual pitch control, shows potential to minimize the increase of, or even reduce, turbine loads.Copyright © 2014 John Wiley & Sons, Ltd.     

基于偏航尾流模型的风电场功率协同优化研究

为减小风电场尾流效应的影响,提升风电场整体发电量,提出一种基于偏航尾流模型的风电场功率协同优化方法。首先建立风电场偏航尾流模型,该模型包括用于计算单机组尾流速度分布的Jensen-Gaussian尾流模型、尾流偏转模型及多机组尾流叠加模型,对各机组风轮前来流风速进行求解;再根据来流风速计算风电场输出功率,并以风电场整体输出功率最大为优化目标,利用拟牛顿算法协同优化各机组轴向诱导因子和偏航角度。以4行4列方形布置的16台NREL-5 MW风电机组为对象进行仿真研究。结果表明,所提出的基于偏航尾流模型的风电场功率协同优化方法能显著提升风电场整体输出功率。     

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of a LES technique to characterize the wake deflection of a wind turbine in yaw

When a wind turbine works in yaw, the wake intensity and the power production of the turbine become slightly smaller and a deflection of the wake is induced. Therefore, a good understanding of this effect would allow an active control of the yaw angle of upstream turbines to steer the wake away from downstream machines, reducing its effect on them. In wind farms where interaction between turbines is significant, it is of interest to maximize the power output from the wind farm as a whole and to reduce fatigue loads on downstream turbines due to the increase of turbulence intensity in wakes. A large eddy simulation model with particular wind boundary conditions has been used recently to simulate and characterize the turbulence generated by the presence of a wind turbine and its evolution downstream the machine. The simplified turbine is placed within an environment in which relevant flow properties like wind speed profile, turbulence intensity and the anisotropy of turbulence are found to be similar to the ones of the neutral atmosphere. In this work, the model is used to characterize the wake deflection for a range of yaw angles and thrust coefficients of the turbine. The results are compared with experimental data obtained by other authors with a particle image velocimetry technique from wind tunnel experiments. Also, a comparison with simple analytical correlations is carried out. Copyright © 2009 John Wiley & Sons, Ltd.     

Integrated offshore wind farm design: Optimizing micro‐siting and cable layout simultaneously

Electrical layout and turbine placement are key design decisions in offshore wind farm projects. Increased turbine spacing minimizes the energy losses caused by wake interactions between turbines but requires costlier cables with higher rates of failure. Simultaneous micro‐siting and electrical layout optimization are required to realize all possible savings. The problem is complex, because electrical layout optimization is a combinatorial problem and the computational fluid‐dynamics calculations to approximate wake effects are impossible to integrate into classical optimization. This means that state‐of‐the‐art methods do not generally consider simultaneous optimization and resort to approximations instead. We extend an existing model that successfully optimizes cable design to simultaneously consider micro‐siting. We use Jensen's equations to approximate the wake effect in an efficient manner, calibrating it with years of mast data. The wake effects are precalculated and introduced into the optimization problem. We solve simultaneously for turbine spacing and cable layout, exploiting the tradeoffs between these wind farm features. We use the Barrow Offshore Wind Farm as a case study to demonstrate realizable savings up to 6 MEUR over the lifetime of the plant, although it is possible that unforeseen design constraints have implications for whether the savings seen in our model are fully realizable in the real world. In addition, the model provides insights on the effects of turbine spacing that can be used to simplify the design process or to support negotiations for surface concession at the earlier stages of a project.     

Wake impacts on downstream wind turbine performance and yaw alignment

Aerodynamic wake interaction between commercial scale wind turbines can be a significant source of power losses and increased fatigue loads across a wind farm. Significant research has been dedicated to the study of wind turbine wakes and wake model development. This paper profiles influential wake regions for an onshore wind farm using 6 months of recorded SCADA (supervisory control and data acquisition) data. An average wind velocity deficit of over 30% was observed corresponding to power coefficient losses of 0.2 in the wake region. Wind speed fluctuations are also quantified for an array of turbines, inferring an increase in turbulence within the wake region. A study of yaw data within the array showed turbine nacelle misalignment under a range of downstream wake angles, indicating a characteristic of wind turbine behaviour not generally considered in wake studies. The turbines yaw independently in order to capture the increased wind speeds present due to the lateral influx of turbulent wind, contrary to many experimental and simulation methods found in the literature. Improvements are suggested for wind farm control strategies that may improve farm‐wide power output. Additionally, possible causes for wind farm wake model overestimation of wake losses are proposed.Copyright © 2012 John Wiley & Sons, Ltd.     

Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm

This paper investigates wake effects on load and power production by using the dynamic wake meander (DWM) model implemented in the aeroelastic code HAWC2. The instationary wind farm flow characteristics are modeled by treating the wind turbine wakes as passive tracers transported downstream using a meandering process driven by the low frequent cross‐wind turbulence components. The model complex is validated by comparing simulated and measured loads for the Dutch Egmond aan Zee wind farm consisting of 36 Vestas V90 turbine located outside the coast of the Netherlands. Loads and production are compared for two distinct wind directions—a free wind situation from the dominating southwest and a full wake situation from northwest, where the observed turbine is operating in wake from five turbines in a row with 7D spacing. The measurements have a very high quality, allowing for detailed comparison of both fatigue and min–mean–max loads for blade root flap, tower yaw and tower bottom bending moments, respectively. Since the observed turbine is located deep inside a row of turbines, a new method on how to handle multiple wakes interaction is proposed. The agreement between measurements and simulations is excellent regarding power production in both free and wake sector, and a very good agreement is seen for the load comparisons too. This enables the conclusion that wake meandering, caused by large scale ambient turbulence, is indeed an important contribution to wake loading in wind farms. Copyright © 2012 John Wiley & Sons, Ltd.     

Maximum power‐point tracking control for wind farms

This paper presents a data‐driven adaptive scheme to adjust the control settings of each wind turbine in a wind farm such that an increase in the total power production of the wind farm is achieved. This is carried out by taking into account the interaction between the turbines through wake effects. The optimization scheme is designed in such a way that it yields fast convergence so that it can adapt to changing wind conditions quickly. The scheme has a distributed architecture in which each wind turbine adapts its control settings through gradient‐based optimization, using information that it receives from neighbouring turbines. The novel control method is tested in a simulation of the Princess Amalia Wind Park. It is shown that the distributed gradient‐based approach performs the optimization in a more time‐efficient manner compared with an existing data‐driven wind farm power optimization method that uses a game theoretic approach. Copyright © 2014 John Wiley & Sons, Ltd.     

The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms

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

An experimental investigation on the effect of individual turbine control on wind farm dynamics

Individual wind turbines in a wind farm typically operate to maximize their performance with no consideration of the impact of wake effects on downstream turbines. There is potential to increase power and reduce structural loads within a wind farm by properly coordinating the turbines. To effectively design and analyze coordinated wind turbine controllers requires control‐oriented turbine wake models of sufficient accuracy. This paper focuses on constructing such a model from experiments. The experiments were conducted to better understand the wake interaction and impact on voltage production in a three‐turbine array. The upstream turbine operating condition was modulated in time, and the dynamic impact on the downstream turbine was recorded through the voltage output time signal. The flow dynamics observed in the experiments were used to improve a static wake model often used in the literature for wind farm control. These experiments were performed in the atmospheric boundary layer wind tunnel at the Saint Anthony Falls Laboratory at the University of Minnesota using particle image velocimetry for flow field analysis and turbine voltage modulation to capture the physical evolution in addition to the dynamics of turbine wake interactions. Copyright © 2015 John Wiley & Sons, Ltd.     

Global sensitivity analysis of wind turbine power output

The dynamics of wind turbine behavior are complex and a critical area of study for the wind industry. Identification of factors that cause changes in turbine performance can sometimes prove to be challenging, whereas other times, it can be intuitive. The quantification of the effect that these factors have is valuable for making improvements to both power performance and turbine health. In commercial farms, large quantities of meteorological and performance data are commonly collected to monitor daily operations. These data can also be used to analyze the relationship between each parameter in order to better understand the interactions that occur and the information contained within these signals. In this global sensitivity analysis, a neural network is used to model select wind turbine supervisory control and data acquisition system parameters for an array of turbines from a commercial wind farm that exhibit signs of wake interaction. An extended Fourier amplitude sensitivity test is then performed for 2 years of 10‐min averaged data. The study examines the primary and combined sensitivities of power output to each selected parameter for two turbines in the array. The primary sensitivities correspond to single parameter interactions, whereas combined sensitivities account for interactions between multiple parameters simultaneously. Highly influential parameters such as wind speed and rotor rotation frequency produce expected results; the extended Fourier amplitude sensitivity test method proved effective at quantifying the sensitivity of a wide range of more subtle inputs. These include blade pitch, yaw position, main bearing and ambient temperatures as well as wind speed and yaw position standard deviation. The technique holds promise for application in full‐scale wake studies where it might be used to determine the benefits of emerging power optimization strategies such as active wake management. The field of structural health monitoring can also benefit from this method. Copyright © 2013 John Wiley & Sons, Ltd.     

Potential of power gain with improved yaw alignment

One of the primary criteria for extracting energy from the wind using horizontal axis upwind wind turbines is the ability to align the rotor axis with the dominating wind direction. The conventional way of estimating the direction of the incoming flow is by using transducers placed atop the nacelle and downwind of the rotor. Recent studies have suggested methods based on advanced upwind measurement technologies for estimating the inflow direction and improving the yaw alignment. In this study, the potential of increased power output with improved yaw alignment is investigated by assessing the performance of a current measurement and yaw control system. The performance is assessed by analyzing data containing upwind wind speed and direction measurements from a met mast, and yaw angle and power production measurements from an operating offshore wind turbine. The results of the analysis indicate that the turbine is operating with a wind speed‐dependent yaw error distribution. The theoretical annual energy production loss due to the yaw error distribution of the existing system is estimated to approximately 0.2%. Copyright © 2014 John Wiley & Sons, Ltd.     

Active wake control: An approach to optimize the lifetime operation of wind farms

The wind turbines within a wind farm impact each other's power production and loads through their wakes. Wake control strategies, aiming to reduce wake effects, receive increasing interest by both the research community and the industry. A number of recent simulation studies with high fidelity wake models indicate that wake mitigation control is a very promising concept for increasing the power production of a wind farm and/or reducing the fatigue loading on wind turbines' components. The purpose of this paper is to study the benefits of wake mitigation control in terms of lifetime power production and fatigue loading on several existing full‐scale commercial wind farms with different scale, layouts, and turbine sizes. For modeling the wake interactions, Energy Research Centre of the Netherlands' FarmFlow software is used: a 3D parabolized Navier‐Stokes code, including a k‐? turbulence model. In addition, an optimization approach is proposed that maximizes the lifetime power production, thereby incorporating the fatigue loads into the optimization criterion in terms of a lifetime extension factor.     

A numerical study of tilt‐based wake steering using a hybrid free‐wake method

This study investigates the potential of using tilt‐based wake steering to alleviate wake shielding problems experienced by downwind turbines. Numerical simulations of turbine wakes have been conducted using a hybrid free‐wake analysis combining vortex lattice method (VLM) and an innovative free‐wake model called constant circulation contour method (CCCM). Simulation results indicate tilting a horizontal axis wind turbine's shaft upward causes its wake to ascend, carrying energy‐depleted air upward and pumping more energetic replacement air into downstream turbines, thereby having the potential to recover downstream turbine power generation. Wake cross section vorticity and velocity distributions reveal that the wake upward transport is caused by the formation of near‐wake streamwise vorticity components, and furthermore, the wake velocity deficit is weakened because of the skewed wake structure. Beyond the single turbine wake simulation, an inline two‐turbine case is performed as an assessment of the wake steering influence on the two‐turbine system and as an exploratory work of simulating turbine‐wake interactions using the hybrid free‐wake model. Individual and total turbine powers are calculated. A comparison between different tilting angles suggests turbine power enhancement may be achieved by tilting the upstream turbine and steering its wakes away from the downstream turbine.     

Comparison and testing of power reserve control strategies for grid‐connected wind turbines

The stability of the electrical grid depends on enough generators being able to provide appropriate responses to sudden losses in generation capacity, increases in power demand or similar events. Within the United States, wind turbines largely do not provide such generation support, which has been acceptable because the penetration of wind energy into the grid has been relatively low. However, frequency support capabilities may need to be built into future generations of wind turbines to enable high penetration levels over approximately 20%. In this paper, we describe control strategies that can enable power reserve by leaving some wind energy uncaptured. Our focus is on the control strategies used by an operating turbine, where the turbine is asked to track a power reference signal supplied by the wind farm operator. We compare the strategies in terms of their control performance as well as their effects on the turbine itself, such as the possibility for increased loads on turbine components. It is assumed that the wind farm operator has access to the necessary grid information to generate the power reference provided to the turbine, and we do not simulate the electrical interaction between the turbine and the utility grid. Copyright © 2013 John Wiley & Sons, Ltd.     

Large eddy simulation of dynamically controlled wind turbines in an offshore environment

Accurate modelling of transient wind turbine wakes is an important component in the siting of turbines within wind farms because of wake structures that affect downwind turbine performance and loading. Many current industry tools for modelling these effects are limited to empirically derived predictions. A technique is described for coupling transient wind modelling with an aero‐elastic simulation to dynamically model both turbine operation and wake structures. The important feature of this approach is a turbine model in a flow simulation, which actively responds to transient wind events through the inclusion of controller actions such as blade pitching and regulation of generator torque. The coupled nature of the aero‐elastic/flow simulation also allows recording of load and control data, which permits the analysis of turbine interaction in multiple turbine systems. An aero‐elastic turbine simulation code and a large eddy simulation (LES) solver using an actuator disc model were adapted for this work. Coupling of the codes was implemented with the use of a software framework to transfer data between simulations in a synchronous manner. A computationally efficient simulation was developed with the ability to model turbines exhibiting standard baseline control operating in an offshore environment. Single and multiple wind turbine instances were modelled in a transient flow domain to investigate wake structures and wake interaction effects. Blade loading data were analysed to quantify the increased fluctuating loads on downwind turbines. The results demonstrate the successful implementation of the coupled simulation and quantify the effect of the dynamic‐turbine model. Copyright © 2012 John Wiley & Sons, Ltd.     

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.     

Quantification of power losses due to wind turbine wake interactions through SCADA,meteorological and wind LiDAR data

Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.     

海上浮式风力机变桨距控制研究

借鉴陆上风力机基于状态空间法的干扰自适应控制,在状态空间中考虑海上浮式风力机支撑平台运动自由度,采用状态观测器评估系统各状态量,研究不同干扰自适应变桨距控制对海上浮式风力机控制性能的影响,并与FAST基础控制对比分析。结果表明:海上浮式风力机变桨距控制设计应基于状态空间法的干扰自适应控制,同时在状态空间中应考虑平台纵荡、纵摇及艏摇自由度。