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.     

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.     

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.     

Robust gain scheduling baseline controller for floating offshore wind turbines

The possibility of a pitch instability for floating wind turbines, due to the blade‐pitch controller, has been discussed extensively in recent years. Contrary to many advanced multi‐input‐multi‐output controllers that have been proposed, this paper aims at a standard proportional‐integral type, only feeding back the rotor speed error. The advantage of this controller is its standard layout, equal to onshore turbines, and the clearly defined model‐based control design procedure, which can be fully automated. It is more robust than most advanced controllers because it does not require additional signals of the floating platform, which make controllers often sensitive to unmodeled dynamics. For the design of this controller, a tailored linearized coupled dynamic model of reduced order is used with a detailed representation of the hydrodynamic viscous drag. The stability margin is the main design criterion at each wind speed. This results in a gain scheduling function, which looks fundamentally different than the one of onshore turbines. The model‐based controller design process has been automated, dependent only on a given stability margin. In spite of the simple structure, the results show that the controller performance satisfies common design requirements of wind turbines, which is confirmed by a model of higher fidelity than the controller design model. The controller performance is compared against an advanced controller and the fixed‐bottom version of the same turbine, indicating clearly the different challenges of floating wind control and possible remedies.     

Field testing of linear individual pitch control on the two‐bladed controls advanced research turbine

This paper presents the results of field tests using linear individual pitch control (LIPC) on the two‐bladed Controls Advanced Research Turbine 2 (CART2) at the National Renewable Energy Laboratory (NREL). LIPC has recently been introduced as an alternative to the conventional individual pitch control (IPC) strategy for two‐bladed wind turbines. The main advantage of LIPC over conventional IPC is that it requires, at most, only two feedback loops to potentially reduce the periodic blade loads. In previous work, LIPC was designed to implement blade pitch angles at a fixed frequency [e.g., the once‐per‐revolution (1P) frequency], which made it only applicable in above‐rated wind turbine operating conditions. In this study, LIPC is extended to below‐rated operating conditions by gain scheduling the controller on the rotor speed. With this extension, LIPC and conventional IPC are successfully applied to the NREL CART2 wind turbine. The field‐test results obtained during the measurement campaign indicate that LIPC significantly reduces the wind turbine loads for both below‐rated and above‐rated operation. Copyright © 2015 John Wiley & Sons, Ltd.     

Model predictive control of a wind turbine using short‐term wind field predictions

As wind turbines become larger and hence more flexible, the design of advanced controllers to mitigate fatigue damage and optimise power capture is becoming increasingly important. The majority of the existing literature focuses on feedback controllers that use measurements from the turbine itself and possibly an estimate or measurement of the current local wind profile. This work investigates a predictive controller that can use short‐term predictions about the approaching wind field to improve performance by compensating for measurement and actuation delays. Simulations are carried out using the FAST aeroelastic design code modelling the NREL 5 MW reference turbine, and controllers are designed for both above rated and below rated wind conditions using model predictive control. Tests are conducted in various wind conditions and with different future wind information available. It is shown that in above rated wind conditions, significant fatigue load reductions are possible compared with a controller that knows only the current wind profile. However, this is very much dependent on the speed of the pitch actuator response and the wind conditions. In below rated wind conditions, the goals of power capture and fatigue load control were considered separately. It was found that power capture could only be improved using wind predictions if the wind speed changed rapidly during the simulation and that fatigue loads were not consistently reduced when wind predictions were available, indicating that wind predictions are of limited benefit in below rated wind conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

大型风电机组模型预测独立变桨控制器设计

针对恒转速运行时,大型风电机组承受的不平衡载荷问题,提出一种多输入多输出的风电机组模型预测(MPC)独立变桨控制策略.首先,建立风电机组旋转坐标系下的状态空间模型,经过坐标变换得到固定坐标系下的平均周期模型,分析表明模型在非对角存在无法忽视的耦合;然后,计算所需观测器和控制器的参数,进一步设计基于Kalman状态观测器...     

Analysis of light detection and ranging wind speed measurements for wind turbine control

Light detection and ranging (LIDAR) systems are able to measure the speed of incoming wind before it reaches a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine structural loads. However, the degree to which such preview‐based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. This study examines the accuracy of different measurement scenarios that rely on coherent continuous‐wave or pulsed Doppler LIDAR systems, in terms of root‐mean‐square measurement error, to determine their applicability to feedforward control. In particular, the impacts of measurement range, angular offset of the LIDAR beam from the wind direction, and measurement noise are studied for various wind conditions. A realistic simulation case involving a scanning LIDAR unit mounted in the spinner of a MW‐scale wind turbine is studied in depth, with emphasis on preview distances that provide minimum measurement error for a specific scan radius. Measurement error is analyzed for LIDAR‐based estimates of point wind speeds at the rotor as well as spanwise averaged blade effective wind speeds. The impact of turbulence structures with high coherent turbulent kinetic energy on measurement error is discussed as well. Copyright © 2013 John Wiley & Sons, Ltd.     

极端风况下双转子风力机降载复合控制策略

为降低双转子风力机在极端风况下的大波动载荷,基于双转子风力机气动与控制仿真系统,提出了基于独立变桨自抗扰控制器和偏航模糊控制器的降载复合控制策略,并分析了正常风况和极端风况下该策略的控制效果。结果表明：与传统PID独立变桨控制相比,在极端运行阵风和极端湍流模型下,独立变桨自抗扰控制方法使叶根挥舞弯矩标准差减小18%以上;与传统恒速偏航控制相比,在极端风向变化下,偏航模糊控制方法使偏航轴承滚动力矩标准差减小约27%。降载复合控制策略有效降低了极端风况下双转子风力机的大载荷,抑制了功率波动。     

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.     

Experimental validation of model‐based blade pitch controller design for floating wind turbines: system identification approach

This paper discusses the model‐based design of a blade pitch controller for a floating offshore wind turbine (FOWT) scale model. A mathematical model of the FOWT is constructed from an input–output measurement in an experiment using system identification. The blade pitch controller is designed by an control method, and the effectiveness of the controller is evaluated by means of a basin experiment using the FOWT scale model. The results show that the blade pitch controller is effective in reducing platform pitch motion and rotor speed fluctuation. Copyright © 2017 John Wiley & Sons, Ltd.     

Wind turbine load reduction by rejecting the periodic load disturbances

For the cost per kilowatt hour to be decreased, the trend in offshore wind turbines is to increase the rotor diameter as much as possible. The increasing dimensions have led to a relative increase of the loads on the wind turbine structure; thus, it is necessary to react to disturbances in a more detailed way, e.g. each blade separately. The disturbances acting on an individual wind turbine blade are to a large extent deterministic; for instance, tower shadow, wind shear, yawed error and gravity are depending on the rotational speed and azimuth angle and will change slowly over time. This paper aims to contribute to the development of individually pitch‐controlled blades by proposing a lifted repetitive controller that can reject these periodic load disturbances for modern fixed‐speed wind turbines and modern variable‐speed wind turbines operating above‐rated. The performance of the repetitive control method is evaluated on the UPWIND 5 MW wind turbine model and compared with typical individual pitch control. Simulation results indicate that for relatively slow changing periodic wind disturbances, this lifted repetitive control method can significantly reduce the vibrations in the wind turbine structure with considerably less high‐frequent control action. 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.     

Identification in closed‐loop operation of models for collective pitch robust controller design

To achieve load reduction and power optimization, wind turbine controllers design requires the availability of reliable control‐oriented linear models. These are needed for model‐based controller design. Model identification of wind turbine while operating in closed loop is an appropriate solution that has recently shown its capabilities when linear time‐invariant controllers and complicated control structures are present. However, the collective pitch control loop, one of the most important wind turbine loops, uses non‐linear controllers. Typically, this non‐linear controller is a combination of a linear controller and a gain scheduling. This paper presents a new algorithm for identification in closed‐loop operation that allows the use of this kind of non‐linear controllers. The algorithm is applied for identification the collective pitch demand to generator speed of a wind turbine at various operating points. The obtained models are presented and discussed from a control point of view. The validity of these models is illustrated by their use for the design of a linear fix robust controller. The performance based on simulation data of this linear controller is similar to that obtained with simulations based on a linear controller with gain scheduling, but its design and implementation is much simpler. Copyright © 2012 John Wiley & Sons, Ltd.     

基于RBF神经网络整定PID的 风力发电变桨距控制

为了改善变速恒频风力发电系统在恒功率输出运行区域内的动态性能,在分析系统变桨距控制研究现状的基础上,基于RBF神经网络（RBFNN）整定PID控制理论设计风力发电系统变桨距控制器,建立了风力机及变桨距机构模型,以发电机转速测量值与额定转速相比后误差为输入设计控制器。在随机风作用下对设计的RBFNN整定PID控制器进行仿真,结果表明基于RBFNN整定PID控制理论的变桨距控制器具有良好的动态性能及对风速扰动的鲁捧性,能够有效改善风力发电系统变桨距控制效果。     

Model‐based receding horizon control of wind farms for secondary frequency regulation

In this study, we propose the use of model‐based receding horizon control to enable a wind farm to provide secondary frequency regulation for a power grid. The controller is built by first proposing a time‐varying one‐dimensional wake model, which is validated against large eddy simulations of a wind farm at startup. This wake model is then used as a plant model for a closed‐loop receding horizon controller that uses wind speed measurements at each turbine as feedback. The control method is tested in large eddy simulations with actuator disk wind turbine models representing an 84‐turbine wind farm that aims to track sample frequency regulation reference signals spanning 40 min time intervals. This type of control generally requires wind turbines to reduce their power set points or curtail wind power output (derate the power output) by the same amount as the maximum upward variation in power level required by the reference signal. However, our control approach provides good tracking performance in the test system considered with only a 4% derate for a regulation signal with an 8% maximum upward variation. This performance improvement has the potential to reduce the opportunity cost associated with lost revenue in the bulk power market that is typically associated with providing frequency regulation services. Copyright © 2017 John Wiley & Sons, Ltd.     

基于模糊控制的风电机组独立变桨距控制

在额定风速以上时,通常采用变桨距控制技术调节大型风电机组来稳定其输出功率.由于风力发电系统的数学模型具有高度非线性、多变量、强耦合的特点,风速又具有多变性,因此文章在分析传统的PID变桨距控制技术优缺点的基础上,提出了基于三维模糊自适应PID控制的独立变桨距控制技术,并且引入风速的模糊前馈控制技术.对1 MW风电机组进行仿真,结果表明,在额定风速以上时,该方法不仅能稳定风电机组的输出功率,而且可以减小桨叶的拍打振动.     

Stability analysis of pitch‐regulated,variable‐speed wind turbines in closed loop operation using a linear eigenvalue approach

A multi‐body aeroelastic design code based on the implementation of the combined aeroelastic beam element is extended to cover closed loop operation conditions of wind turbines. The equations of a controller for variable generator speed and pitch‐controlled operation in high wind speeds are combined with the aeroelastic equations of motion for the complete wind turbine, in order to provide a compound aeroservoelastic system of equations. The control equations comprise linear differential equations for the pitch and generator torque actuators, the control feedback elements (proportional–integral control) and the various filters acting on the feedback signals. In its non‐linear form, the dynamic equations are integrated in time to provide the reference state, while upon linearization of the system and transformation in the non‐rotating frame, the linear stability equations are derived. Stability results for a multi‐MW wind turbine show that the coupling of the controller dynamics with the aeroelastic dynamics of the machine is important and must be taken into account in view of defining the controller parameters. Copyright © 2008 John Wiley & Sons, Ltd.     

Model predictive control for wind turbines with distributed active flaps: incorporating inflow signals and actuator constraints

This paper describes the implementation of system identification and controller design techniques using model predictive control (MPC) for wind turbines with distributed active flaps for load control. An aeroservoelastic model of the 5 MW NREL/Upwind reference wind turbine, implemented in the code DU_SWAMP, is used in an industry‐based MPC controller design cycle, involving the use of dedicated system identification techniques. The novel multiple‐input multiple‐output MPC controllers, which incorporate flap actuator constraints and the use of local inflow measurement signals, are designed and implemented for various operating points. The controllers are evaluated in standard power production load cases and fatigue load reductions up to 27.3% are achieved. The distributed flaps controller scheme is also compared with simpler single‐flap single‐input single‐output and individual pitch controller schemes. Copyright © 2011 John Wiley & Sons, Ltd.