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.     

Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects called the FLOw Redirection and Induction in Steady‐state (FLORIS) model. The FLORIS model predicts the steady‐state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high‐fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Response of stall‐controlled,teetered, free‐yaw downwind turbines

Stall‐controlled, teetered, free‐yaw downwind turbines have historically experienced higher than desired peak loads due to occasional large teeter excursions when the blades are stalled. Understanding of the causes of these excursions and the potential solution have been the subject of much research in the past. This article contributes to this study in three areas. One is the development of the equations of motion for a teetered rotor with a delta‐3 angle. The solution of these equations leads to an improved understanding of the mechanics of these types of rotors and to a theory concerning how the rotor responds to different overall aerodynamic moments. A second contribution is a set of test cases to confirm the theories developed and to show how wind speed and different delta‐3 angles affect the restoring moments on the rotor and nacelle. A final contribution is an ADAMS^® analysis of the fatigue loading of the AWT‐27 under high turbulent wind speeds with different delta‐3 angles and the comparison with results of a field testing programme. Both the predictions and the field data point to the more benign response of a hub having a negative delta‐3 angle. This contradicts previous opinions but is consistent with the criteria presented in this article. Copyright © 1999 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.     

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.     

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.     

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.     

Vibration response of a 2.3 MW wind turbine to yaw motion and shut down events

Structural health monitoring (SHM) is a process of implementing a damage detection strategy for a mechanical system. Wind turbine machinery stands to benefit from SHM significantly as the ability to detect early stages of damage before significant malfunction or structural failure occurs would reduce costs of wind power projects by reducing maintenance costs. Vibration analysis of dynamic structural response is an approach to SHM that has been successfully applied to mechanical and civil systems and shows some promise for wind turbine application. Traditionally, a setback to turbine vibration‐based SHM techniques has been the unavailability of turbine vibration response data. This study begins to address this issue by presenting vibration response for a commercial 2.3 MW turbine to a limited number of operating conditions. A database of acquired vibration response signals detailing turbine response to yaw motion, start‐up, operation and shutdown has been assembled. A Daubechies sixth‐order wavelet was used to perform an eight‐level discrete wavelet decomposition such that general trends and patterns within the signals could be identified. With further development, the presented analysis of vibration response may be integrated into routines to reduce downtime and failure frequency of utility scale wind turbines. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

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.     

风电机组振动数据分析与偏航校正

风电机组运行数据能够反映各系统之间的相关性和机组运行中存在的问题。为了判断机组发电效率低的原因,本文根据机舱振动加速度数据提出假定并通过计算分析风速功率曲线和偏航误差角度对应关系。现场检查结果表明,该机组偏航存在45°误差并导致振动异常,与理论推算误差角度44.6°相符。     

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.     

Near wake Reynolds‐averaged Navier–Stokes predictions of the wake behind the MEXICO rotor in axial and yawed flow conditions

In the present paper, Reynolds‐averaged Navier–Stokes predictions of the flow field around the MEXICO rotor in yawed conditions are compared with measurements. The paper illustrates the high degree of qualitative and quantitative agreement that can be obtained for this highly unsteady flow situation, by comparing measured and computed velocity profiles for all three Cartesian velocity components along four axial transects and several radial transects. Copyright © 2012 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.     

Light detection and ranging measurements of wake dynamics part I: one‐dimensional scanning

The vast majority of wind turbines are today erected in wind farms. As a consequence, wake‐generated loads are becoming more and more important. In this first of two parts, we present a new experimental technique to measure the instantaneous wake deficit directly, thus allowing for quantification of the wake meandering, as well as the instantaneous wake expansion expressed in a meandering frame of reference. The experiment was conducted primarily to test the simple hypothesis that the wake deficit is advected passively by the larger‐than‐rotor‐size eddies in the atmospheric flow, and that the wake at the same time widens gradually, primarily because of mixing caused by small‐scale atmospheric eddies. In this first paper, we focus on our new measurement technique, and test if the wake meandering follows the wind direction fluctuations, i.e. if it is advected passively in the lateral direction. The experimental results are used as a preliminary verification of a wake meandering model that essentially considers the wake as a passive tracer. Copyright © 2009 John Wiley & Sons, Ltd.     

Quantitative detection method for icing of horizontal‐axis wind turbines

Icing seriously endangers the operational safety of wind turbines, and there has been a lack of research on the quantitative detection and early warning of the icing distribution on a blade. In order to address the present state and specific engineering problems of wind turbines, a method based on external sensor installation is proposed for quantitatively monitoring the icing distribution on a blade through numerical simulation and sensor detection technology. Field tests were performed on 1.5 and 2.0 MW wind turbines in icy weather. The detected distribution and thickness of the blade icing showed good agreement with the field results, which verifies the effectiveness of the detection method. The error between the detected and real ice thicknesses was approximately 20%, which breaks through the limitations of previous qualitative monitoring, and the error for specific ice formations on the blade was within the acceptable range. This new monitoring method can provide a reference for wind turbine icing detection and technical support for the efficient design and effective operation of icing protection facilities.     

Numerical error estimation of conventional anemometry mounted on offshore floating met‐masts

Offshore wind energy is moving towards a future where the main challenge is to cope with the increasing water depth needed to access more and better wind resources. One of the first steps to be undertaken is the development of floating structures to support either wind turbines or measurement devices for the proper characterization of wind energy resources. The use of floating devices for measuring wind speed involves a number of uncertainties not presented by seabed fixed systems. These sources of uncertainty or error are present both in instantaneous wind measurements and averaged (10 min or hourly) values because of (i) variability in the measurement height, (ii) the tilt of the anemometer and (iii) the relative velocity between the anemometer and the wind, among others. In this paper, a methodology for assessing the error in the wind measurement characterization because of the movement of a floating meteorological mast is presented. By the numerical simulation of a floating mast, the short‐ and long‐term error in the characterization of the wind at different heights has been evaluated. In general, the error because of the tilt can reach up to 80% of the total error; the error because of the variation of the vertical position of the anemometer reaches values of up to 15% in some cases; moreover, the error associated with the relative velocity between the anemometer and the wind, for averaged values, is significantly less. Finally, it can be concluded that the total error is lower than 0.5% for 10 min averaged wind speed of up to 24 m/s. Copyright © 2016 John Wiley & Sons, Ltd.     

Shake table testing and numerical simulation of a utility‐scale wind turbine including operational effects

Shake table tests were undertaken on an actual wind turbine (65 kW rated power, 22.6 m hub height and a 16 m rotor diameter) using the Network for Earthquake Engineering Simulation Large High Performance Outdoor Shake Table at the University of California, San Diego. Each base shaking event was imparted in two states, whereas the turbine rotor was still (parked), and while it was spinning (operational). Each state was tested in two orientations of shaking direction, one parallel (fore‐aft) and another perpendicular (side‐to‐side) to the axis of rotation of the rotor. Structural response characteristics are presented for motions imparted in both configurations and both operational states. Modal parameters (natural frequencies, damping ratios and mode shapes) were estimated throughout the testing program. It is found that shaking imparted in the fore‐aft direction while spinning is the only observed situation where operational effects appear significant, with reductions up to 33% in seismic bending moment demand near the tower base. Using modifications developed by the research team to the FAST code, experimental results are compared with corresponding simulations to show that dynamic characteristics, acceleration time histories and trends in tower bending seismic demand can be numerically approximated. This experimental evidence and associated numerical simulations suggest that modeling of combined wind and earthquake loading with existing turbine specific codes produce meaningful results. Discrepancies between experimental and numerical results support that further refinement of simulation codes can improve accuracy beyond the current state. Copyright © 2013 John Wiley & Sons, Ltd.     

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.