Optimal blade pitch control with realistic preview wind measurements

In above‐rated wind speeds, the goal of a wind turbine blade pitch controller is to regulate rotor speed while minimizing structural loads and pitch actuation. This controller is typically feedback only, relying on a generator speed measurement, and sometimes strain gages and accelerometers. A preview measurement of the incoming wind speed (from a turbine‐mounted lidar, for example) allows the addition of feedforward control, which enables improved performance compared with feedback‐only control. The performance improvement depends both on the amount of preview time available in the wind speed measurement and the coherence between the wind measurement and the wind that is actually experienced by the turbine. We show how to design a collective‐pitch optimal controller that takes both of these factors into account. Simulation results show significant improvement compared with baseline controllers and are well correlated with linear model‐based results. Linear model‐based results show the benefit of preview measurements for various preview times and measurement coherences. Copyright © 2016 John Wiley & Sons, Ltd.     

Nonlinear model predictive control of wind turbines using LIDAR

LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts in wind energy such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller with a baseline controller, showing the advantages of using the wind predictions in the optimization problem to reduce wind turbine extreme and fatigue loads on tower and blades as well as to limit the pitch rates. The wind information is obtained by a detailed simulation of a LIDAR system. The controller design is evaluated and tested in a simulation environment with coherent gusts and a set of turbulent wind fields using a detailed aeroelastic model of the wind turbine over the full operation region. Results show promising load reduction up to 50% for extreme gusts and 30% for lifetime fatigue loads without negative impact on overall energy production. This controller can be considered as an upper bound for other LIDAR assisted controllers that are more suited for real time applications. Copyright © 2012 John Wiley & Sons, Ltd.     

LIDAR-assisted feedforward individual pitch control of a 15 MW floating offshore wind turbine

Nacelle-mounted, forward-facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above-rated wind speed conditions. Large-scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor-swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR-assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi-beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR-assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above-rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms⁻¹, the LIDAR-assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.     

Load alleviation of wind turbines by yaw misalignment

Vertical wind shear is one of the dominating causes of load variations on the blades of a horizontal axis wind turbine. To alleviate the varying loads, wind turbine control systems have been augmented with sensors and actuators for individual pitch control. However, the loads caused by a vertical wind shear can also be affected through yaw misalignment. Recent studies of yaw control have been focused on improving the yaw alignment to increase the power capture at below rated wind speeds. In this study, the potential of alleviating blade load variations induced by the wind shear through yaw misalignment is assessed. The study is performed through simulations of a reference turbine. The study shows that optimal yaw misalignment angles for minimizing the blade load variations can be identified for both deterministic and turbulent inflows. It is shown that the optimal yaw misalignment angles can be applied without power loss for wind speeds above rated wind speed. In deterministic inflow, it is shown that the range of the steady‐state blade load variations can be reduced by up to 70%. For turbulent inflows, it is shown that the potential blade fatigue load reductions depend on the turbulence level. In inflows with high levels of turbulence, the observed blade fatigue load reductions are small, whereas the blade fatigue loads are reduced by 20% at low turbulence levels. For both deterministic and turbulent inflows, it is seen that the blade load reductions are penalized by increased load variations on the non‐rotating turbine parts. Copyright © 2013 John Wiley & Sons, Ltd.     

Influence of atmospheric stability on wind turbine loads

Simulations of wind turbine loads for the NREL 5 MW reference wind turbine under diabatic conditions are performed. The diabatic conditions are incorporated in the input wind field in the form of wind profile and turbulence. The simulations are carried out for mean wind speeds between 3 and 16 m s ^{? 1} at the turbine hub height. The loads are quantified as the cumulative sum of the damage equivalent load for different wind speeds that are weighted according to the wind speed and stability distribution. Four sites with a different wind speed and stability distribution are used for comparison. The turbulence and wind profile from only one site is used in the load calculations, which are then weighted according to wind speed and stability distributions at different sites. It is observed that atmospheric stability influences the tower and rotor loads. The difference in the calculated tower loads using diabatic wind conditions and those obtained assuming neutral conditions only is up to 17%, whereas the difference for the rotor loads is up to 13%. The blade loads are hardly influenced by atmospheric stability, where the difference between the calculated loads using diabatic and neutral input wind conditions is up to 3% only. The wind profiles and turbulence under diabatic conditions have contrasting influences on the loads; for example, under stable conditions, loads induced by the wind profile are larger because of increased wind shear, whereas those induced by turbulence are lower because of less turbulent energy. The tower base loads are mainly influenced by diabatic turbulence, whereas the rotor loads are influenced by diabatic wind profiles. The blade loads are influenced by both, diabatic wind profile and turbulence, that leads to nullifying the contrasting influences on the loads. The importance of using a detailed boundary‐layer wind profile model is also demonstrated. The difference in the calculated blade and rotor loads is up to 6% and 8%, respectively, when only the surface‐layer wind profile model is used in comparison with those obtained using a boundary‐layer wind profile model. Finally, a comparison of the calculated loads obtained using site‐specific and International Electrotechnical Commission (IEC) wind conditions is carried out. It is observed that the IEC loads are up to 96% larger than those obtained using site‐specific wind conditions.Copyright © 2012 John Wiley & Sons, Ltd.     

A spinner‐integrated wind lidar for enhanced wind turbine control

A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar‐measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed‐forward enabled turbine controllers is discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

For region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.     

Preventing wind turbine overspeed in highly turbulent wind events using disturbance accommodating control and light detection and ranging

Light detection and ranging (LIDAR) systems can be used to provide wind inflow information to a wind turbine controller before the wind reaches the turbine. Both fatigue and extreme load reduction are possible as a result; in this research, we propose a LIDAR‐based controller designed to prevent emergency shutdowns caused by rotor overspeed. This switching controller consists of a disturbance accommodating control (DAC)‐based baseline controller and a different DAC linearized about a reduced generator speed for extreme events, also referred to as an extreme event controller. Switching between the controllers was performed using linear interpolation over various transition times, depending on how early the extreme event could be detected. If a gust of wind is detected using LIDAR measurements evaluated by a one‐sided cumulative summation algorithm, a relatively long transition time can be used. Switching can also be based on a large output estimation error, ε_y, in which case the transition time is shorter. Once the extreme event passed, control is switched from the extreme event controller back to the baseline DAC. This switching controller resulted in fewer overspeeds when compared with the modified baseline controller, which is a gain scheduled DAC. By preventing overspeeds, the switching controller increased the mean power the wind turbine generated over a simulated 10 min period. Copyright © 2014 John Wiley & Sons, Ltd.     

Advanced control algorithms for reduction of wind turbine structural loads

To enable further growth of wind turbine dimensions and rated power, it is essential to decrease structural loads that wind turbines experience. Therefore a great portion of research is focused on control algorithms for reduction of wind turbine structural loads, but typically wind turbine rotor is considered to be perfectly symmetrical, and therefore such control algorithms cannot reduce structural loads caused by rotor asymmetries. Furthermore, typical approach in the literature is to use blade load measurements, especially when higher harmonics of structural loads are being reduced. In this paper, improvements to standard approach for reduction of structural loads are proposed. First, control algorithm capable of reducing structural loads caused by rotor asymmetries is developed, and then appropriate load transformations are introduced that enable presented control algorithms to use load measurements from various wind turbine components. Simulation results show that proposed control algorithm is capable of reducing structural loads caused by rotor asymmetries.     

Sensor comparison study for load alleviating wind turbine pitch control

As the size of wind turbines increases, the load alleviating capabilities of the turbine controller are becoming increasingly important. Load alleviating control schemes have traditionally been based on feedback from load sensor; however, recent developments of measurement technologies have enabled control on the basis of preview measurements of the inflow acquired using, e.g., light detection and ranging. The potential of alleviating load variations that are caused by mean wind speed changes through feed‐forward control have been demonstrated through both experiments and simulations in several studies, whereas the potential of preview control for alleviating the load variations caused by azimuth dependent inflow variations is less described. Individual or cyclic pitch is required to alleviate azimuth dependent load variations and is traditionally applied through feedback control of the blade root loads. In many existing studies, the performance of an advanced controller is compared with the performance of a simpler controller. In this study, the effect of three measurement types on the load alleviating performance of the same cyclic pitch control design is studied. By using a baseline cyclic pitch controller as test bench, the effect of the different measurement types on the controller performance can be assessed independent of control design. The three measurement types that are considered in this study are as follows: blade root out‐of‐plane bending moment, on‐blade measurements of angle of attack and relative velocity at a radial position of the blades, and upstream inflow measurements from a spinner mounted light detection and ranging (LiDAR) sensor that enables preview of the incoming flow field. The results show that for stationary inflow conditions, the three different measurement types yield similar load reductions, but for varying inflow conditions, the LiDAR sensor‐based controller yields larger load reductions than the two others. The results also show that the performance of the LiDAR sensor‐based controller is very sensitive to uncertainties relating to the inflow estimation. Copyright © 2013 John Wiley & Sons, Ltd.     

Predictive control of an experimental wind turbine using preview wind speed measurements

The development of a more reliable method of measuring the wind field upstream of a turbine (light detection and ranging) has enabled the implementation of feedforward‐related control strategies to enhance the control performance of wind turbines. By incorporating wind speed measurements, the controller is able to anticipate upon future events and thereby improve structural load mitigation and power regulation of the wind turbine. This work aims to experimentally verify the benefits of using predictive and feedforward‐based control strategies over industry standard control solutions. To achieve this, both a feedforward and a model predictive control strategy are presented, which have been validated on an experimental wind turbine in a wind tunnel. Both the model predictive controller and feedforward algorithm have shown superior performance over a baseline controller in terms of rotor speed regulation under wind speed disturbances. The experiment confirmed that a phase advantage in the control input of the predictive controller led to this performance increase. It has also been found that knowledge of just the current wind speed can already significantly increase the control performance. Copyright © 2014 John Wiley & Sons, Ltd.     

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind‐turbine wakes for different hub‐height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met‐tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub‐height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near‐wake velocity field, while it affects noticeably the far‐wake evolution and recovery. A secondary effect on wake‐recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake‐generated turbulence fosters wake recovery. A semi‐empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models.     

Simulating Feedback Linearization control of wind turbines using high‐order models

As wind turbines are becoming larger, wind turbine control must now encompass load control objectives as well as power and speed control to achieve a low cost of energy. Due to the inherent non‐linearities in a wind turbine system, the use of non‐linear model‐based controllers has the potential to increase control performance. A non‐linear feedback linearization controller with an Extended Kalman Filter is successfully used to control a FAST model of the controls advanced research turbine with active blade, tower and drive‐train dynamics in above rated wind conditions. The controller exhibits reductions in low speed shaft fatigue damage equivalent loads, power regulation and speed regulation when compared to a Gain Scheduled Proportional Integral controller, designed for speed regulation alone. The feedback linearization controller shows better rotor speed regulation than a Linear Quadratic Regulator (LQR) at close to rated wind speeds, but poorer rotor speed regulation at higher wind speeds. This is due to modeling inaccuracies and the addition of unmodeled dynamics during simulation. Similar performance between the feedback linearization controller and the LQR in reducing drive‐train fatigue damage and power regulation is observed. Improvements in control performance may be achieved through increasing the accuracy of the non‐linear model used for controller design. Copyright © 2009 John Wiley & Sons, Ltd.     

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Individual pitch control (IPC) provides an important means of attenuating harmful fatigue and extreme loads upon the load bearing structures of a wind turbine. Conventional IPC architectures determine the additional pitch demand signals required for load mitigation in response to measurements of the flap‐wise blade‐root bending moments. However, the performance of such architectures is fundamentally limited by bandwidth constraints imposed by the blade dynamics. Seeking to overcome this problem, we present a simple solution based upon a local blade inflow measurement on each blade. Importantly, this extra measurement enables the implementation of an additional cascaded feedback controller that overcomes the existing IPC performance limitation and hence yields significantly improved load reductions. Numerical demonstration upon a high‐fidelity and nonlinear wind turbine model reveals (1) 60% reduction in the amplitude of the dominant 1P fatigue loads and (2) 59% reduction in the amplitude of extreme wind shear‐induced blade loads, compared with a conventional IPC controller with the same robust stability margin. This paper therefore represents a significant alternative to wind turbine IPC load mitigation as compared with light detection and ranging‐based feedforward control approaches.     

Blade load mitigation control design for a wind turbine operating in the path of vortices

As wind turbine rotor size continues to increase, load mitigation becomes an important control objective. Turbines with hub heights of nearly 100m operate in the stable, nocturnal boundary layer where coherent turbulence can be generated by atmospheric phenomena outside the surface layer. These coherent turbulent structures may contribute to blade fatigue loads that can be mitigated with advanced control algorithms. Disturbance accommodating control (DAC) methods were implemented in a wind turbine structural dynamics simulation code to mitigate transient blade load response induced by a simple, Rankine vortex in the inflow. As a best‐case scenario, a full‐state feedback controller (which included a very detailed disturbance model) showed that blade flap damage equivalent load caused by the vortex passing through the rotor could be reduced by 30% compared to one that resulted from simulation of a typical proportional‐integral (PI) controller. A realizable DAC controller that incorporates only the vertical shear component of the vortex reduced loads by 9% compared to that resulting from simulation of a PI controller. The load reduction was even greater when the vortex was superimposed over full‐field, homogeneous turbulence. DAC methods have the flexibility to incorporate properties of coherent turbulent inflow structures in the controller design to mitigate blade fatigue loads. Further work must be done to develop disturbance models as more details about the turbulent structures are identified. Copyright © 2007 John Wiley & Sons, Ltd.     

低空急流条件下水平轴风力机风轮气动特性的研究

为阐明低空急流条件下风力机风轮的气动特性,基于工程化的边界层风速模型和Von Karman谱模型建立不同来流的脉动风场,对比研究低空急流条件下NREL 5 MW风力机风轮的输出功率和气动载荷的变化规律。结果表明:如果仅以轮毂高度处的风速作为风力机变桨控制的依据,与均匀来流和剪切来流相比较,低空急流条件下,虽然来流风功率明显增大,但风轮的输出功率在较高风速时反而减小;风轮所受的不平衡气动载荷,包括横向力、纵向力、偏航力矩和倾覆力矩在较高风速时小于剪切来流的结果;且仅以轮毂高度处的风速预测得到的风轮输出功率高于实际结果,其最大相对误差为89.4%。因此,低空急流条件下,为提高风能利用率和风轮输出功率的预测精度,应考虑不同高度位置处的风速大小对风力机进行变桨控制和功率预测。     

On wind turbine loads during the evening transition period

The late afternoon hours in the diurnal cycle precede the development of the nocturnal stable boundary layer. This “evening transition” (ET) period is often when energy demand peaks. This period also corresponds to the time of day that is a precursor to late‐afternoon downbursts, a subject of separate interest. To capture physical characteristics of wind fields in the atmospheric boundary layer (ABL) during this ET period, particularly the interplay of shear and turbulence, stochastic simulation approaches, although more tractable, are not suitable. Large‐eddy simulation (LES), on the other hand, may be used to generate high‐resolution ABL turbulent flow fields. We present a suite of idealized LES four‐dimensional flow fields that define a database representing different combinations of large‐scale atmospheric conditions (characterized by associated geostrophic winds) and surface boundary conditions (characterized by surface heat fluxes). Our objective is to evaluate the performance of wind turbines during the ET period. Accordingly, we conduct a statistical analysis of turbine‐scale wind field variables. We then employ the database of these LES‐based inflow wind fields in aeroelastic simulations of a 5‐MW wind turbine. We discuss how turbine loads change as the ET period evolves. We also discuss maximum and fatigue loads on the rotor and tower resulting from different ABL conditions. Results of this study suggest that, during the ET period, the prevailing geostrophic wind speed affects the mean and variance of longitudinal winds greatly and thus has significant influence on all loads except the yaw moment which is less sensitive to uniform and symmetric incoming flow. On the other hand, surface heat flux levels affect vertical turbulence and wind shear more and, as a result, only affect maximum blade flapwise bending and tower fore‐aft bending loads.     

Preview predictive control layer design based upon known wind turbine blade‐pitch controllers

The use of upstream wind measurements has motivated the development of blade‐pitch preview controllers for improving rotor speed tracking and structural load reduction beyond that achievable via conventional feedback control. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closed‐loop dynamics of the existing blade‐pitch feedback control system. This can result in a deterioration of the robustness properties and performance of the existing feedback control system. Furthermore, performance gains from utilising the upcoming real‐time measurements cannot be easily distinguished from the feedback control, making it difficult to formulate a clear business case for the use of preview control. Therefore, the aim of this work is to formulate a modular MPC layer on top of a given output‐feedback blade‐pitch controller, with a view to retaining the closed‐loop robustness and frequency‐domain performance of the latter. The separate nature of the proposed controller structure enables clear and transparent quantification of the benefits gained by using preview control, beyond that of the underlying feedback controller. This is illustrated by results obtained from high‐fidelity closed‐loop turbine simulations, showing the proposed control scheme incorporating knowledge of the oncoming wind and constraints achieved significant 43% and 30% reductions in the rotor speed and flap‐wise blade moment standard deviations, respectively. Additionally, the chance of constraint violations on the rotor speed decreased remarkably from 2.15% to 0.01%, compared to the nominal controller. Copyright © 2017 John Wiley & Sons, Ltd.     

Turbulence influence on wind energy extraction for a medium size vertical axis wind turbine

The relation between power performance and turbulence intensity for a VAWT H‐rotor is studied using logged data from a 14 month (discontinuous) period with the H‐rotor operating in wind speeds up to 9 m/s. The turbine, designed originally for a nominal power of 200 kW, operated during this period mostly in a restricted mode due to mechanical concerns, reaching power levels up to about 80 kW. Two different approaches are used for presenting results, one that can be compared to power curves consistent with the International Electrotechnical Commission (IEC) standard and one that allows isolating the effect of turbulence from the cubic variation of power with wind speed. Accounting for this effect, the turbine still shows slightly higher efficiency at higher turbulence, proposing that the H‐rotor is well suited for wind sites with turbulent winds. The operational data are also used to create a C_p(λ) curve, showing slightly lower C_p compared with a curve simulated by a double multiple streamtube model. Copyright © 2016 John Wiley & Sons, Ltd.     

An investigation of variable power collective pitch control for load mitigation of floating offshore wind turbines

This paper investigates the loads on offshore floating wind turbines and a new control method that can be used to reduce these loads. In this variable power collective pitch control method, the rated generator speed, which is the set point that the collective pitch control attempts to drive the actual generator speed towards, is no longer a constant value but instead a variable that depends on the platform pitch velocity. At a basic physical level, this controller achieves the following: as the rotor of a floating turbine pitches upwind, the controller adjusts so as to extract more energy from the wind by increasing the rated generator speed and thus damps the motion; as the rotor pitches downwind, less energy is extracted because the controller reduces the rated generator speed and again damps the motion. This method is applied to the NREL 5 MW wind turbine model, in above‐rated conditions where the platform motion is most problematic. The results indicate significant load reductions on key structural components, at the expense of minor increases in power and speed variability. The loads on the blades and tower are investigated more generally, and simple dynamic models are used to gain insight into the behavior of floating wind turbine systems. It is clear that for this particular design, aerodynamic methods for reducing platform motion and tower loads are likely inadequate to allow for a viable design, and so new designs or possibly new control degrees of freedom are needed. Copyright © 2012 John Wiley & Sons, Ltd.