Rotor Effective Wind Speed Research Articles

Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation

The aerodynamic interaction between wind turbines grouped in wind farms results in wake-induced power loss and fatigue loads of wind turbines. To mitigate these, wind farm control should be able to account for those interactions, typically using model-based approaches. Such model-based control approaches benefit from computationally fast, linear models and therefore, in this work, we introduce the Dynamic Flow Predictor. It is a fast, control-oriented, dynamic, linear model of wind farm flow and operation that provides predictions of wind speed and turbine power. The model estimates wind turbine aerodynamic interaction using a linearized engineering wake model in combination with a delay process. The Dynamic Flow Predictor was tested on a two-turbine array to illustrate its main characteristics and on a large-scale wind farm, comparable to modern offshore wind farms, to illustrate its scalability and accuracy in a more realistic scale. The simulations were performed in SimWindFarm with wind turbines represented using the NREL 5 MW model. The results showed the suitability, accuracy, and computational speed of the modeling approach. In the study on the large-scale wind farm, rotor effective wind speed was estimated with a root-mean-square error ranging between 0.8% and 4.1%. In the same study, the computation time per iteration of the model was, on average, 2.1 × 10 − 5 s. It is therefore concluded that the presented modeling approach is well suited for use in wind farm control.

Power Generation Control of a Monopile Hydrostatic Wind Turbine Using an <inline-formula> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$ </tex-math> </inline-formula> Loop-Shaping Torque Controller and an LPV Pitch Controller

We transform the National Renewable Energy Laboratory (NREL) 5-MW geared equipped monopile wind turbine model into a hydrostatic wind turbine (HWT) by replacing its drivetrain with a hydrostatic transmission (HST) drivetrain. Then, we design an $\mathcal {H}_{\infty }$ loop-shaping torque controller (to regulate the motor displacement) and a linear parameter varying (LPV) blade pitch controller for the HWT. To enhance the performances of the pitch control system during the transition region around the rated wind speed, we add an antiwindup (AW) compensator to the LPV controller, which would otherwise have had undesirable system responses due to pitch saturation. The LPV AW pitch controller uses the steady rotor effective wind speed as the scheduling parameter, which is estimated by the light detection and ranging preview. The simulations based on the transformed NREL 5-MW HWT model show that our torque controller achieves a very good tracking behavior, while our pitch controller (no matter with or without AW) gets much improved overall performances over a gain-scheduled PI pitch controller.

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop “Optimizing Lidar Design for Wind Energy Applications” was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.

Local wind speed estimation, with application to wake impingement detection

Abstract Wind condition awareness is an important factor to maximize power extraction, reduce fatigue loading and increase the power quality of wind turbines and wind power plants. This paper presents a new method for wind speed estimation based on blade load measurements. Starting from the definition of a cone coefficient, which captures the collective zeroth-harmonic of the out-of-plane blade bending moment, a rotor-effective wind speed estimator is introduced. The proposed observer exhibits a performance similar to the well known torque balance estimator. However, while the latter only measures the average wind speed over the whole rotor disk, the proposed approach can also be applied locally, resulting in estimates of the wind speed in different regions of the rotor disk. In the present work, the proposed method is used to estimate the average wind speed over four rotor quadrants. The top and bottom quadrants are used for estimating the vertical shear profile, while the two lateral ones for detecting the presence of a wake shed by an upstream wind turbine. The resulting wake detector can find applicability in wind farm control, by indicating on which side of the rotor the upstream wake is impinging. The new approach is demonstrated with the help of field test data, as well as simulations performed with high-fidelity aeroservoelastic models.

Assessing the Severity of Wind Gusts with Lidar

Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.

Estimation of rotor effective wind speeds using autoregressive models on Lidar data

Lidars have become increasingly useful for providing accurate wind speed measurements in front of the wind turbine. The wind field measured at distant meteorological masts changes its structure or was too distorted before it reaches the turbine. Thus, one cannot simply apply Taylor's frozen turbulence for representing this distant flow field at the rotor. Wind turbine controllers can optimize the energy output and reduce the loads significantly, if the wind speed estimates were known in advance with high accuracy and low uncertainty. The current method to derive wind speed estimations from aerodynamic torque, pitch angle and tip speed ratio after the wind field flows past the turbine and have their limitations, e.g. in predicting gusts. Therefore, an estimation model coupled with the measuring capability of nacelle based Lidars was necessary for detecting extreme events and for estimating accurate wind speeds at the rotor disc. Nacelle-mounted Lidars measure the oncoming wind field from utpo 400m(5D) in front of the turbine and appropriate models could be used for deriving the rotor effective wind speed from these measurements. This article proposes an auto-regressive model combined with a method to include the blockage factor in order to estimate the wind speeds accurately using Lidar measurements. An Armax model was used to determine the transfer function that models the physical evolution of wind towards the wind turbine, incorporating the effect of surface roughness, wind shear and wind variability at the site. The model could incorporate local as well as global effects and was able to predict the rotor effective wind speeds with adequate accuracy for wind turbine control actions. A high correlation of 0.86 was achieved as the Armax modelled signal was compared to a reference signal. The model could also be extended to estimate the damage potential during high wind speeds, gusts or abrupt change in wind directions, allowing the controller to act appropriately under extreme conditions.

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.

Detection of Wind Evolution and Lidar Trajectory Optimization for Lidar-Assisted Wind Turbine Control

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of lidar systems can use to improve their devices to better assist preview control concepts.

Power Performance Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar Scanner

Abstract Different certification procedures in wind energy, such as power performance testing or load estimation, require measurements of the wind speed, which is set in relation to the electrical power output or the turbine loading. The wind shear affects the behavior of the turbine as hub heights and rotor diameters of modern wind turbines increase. Different measurement methods have been developed to take the wind shear into account. In this paper an approach is presented where the wind speed is measured from the nacelle of a wind turbine using a scanning lidar system. The measurement campaign was performed on the two-bladed Controls Advanced Research Turbine (CART-2) at the National Wind Technology Center in Colorado. The wind speed of the turbine inflow was measured and recalculated in three different ways: using an anemometer installed on a meteorological mast, using the nacelle-based lidar scanner, and using the wind turbine itself. Here, the wind speed was recalculated from turbine data using the wind turbine as a big horizontal anemometer. Despite the small number of useful data, the correlation between this so-called rotor effective wind speed and the wind speed measured by the scanning nacelle-based lidar is high. It could be demonstrated that a nacelle-based scanning lidar system provides accurate measurements of the wind speed converted by a wind turbine. This is a first step, and it provides evidence to support further investigations using a much more extensive dataset and refines the parameters in the measurement process.

Wind Speed Estimation and Parametrization of Wake Models for Downregulated Offshore Wind Farms within the scope of PossPOW Project

With increasing installed capacity, wind farms are requested to downregulate more frequently, especially in the offshore environment. Determination and verification of possible (or available) power of downregulated offshore wind farms are the aims of the PossPOW project (see PossPOW.dtu.dk). Two main challenges encountered in the project so far are the estimation of wind speed and the recreation of the flow inside the downregulated wind farm as if it is operating ideally. The rotor effective wind speed was estimated using power, pitch angle and rotational speed as inputs combined with a generic Cp model. The results have been compared with Horns Rev-I dataset and NREL 5MW simulations under both downregulation and normal operation states. For the real-time flow recreation, the GCLarsen single wake model was re-calibrated using a 1-s dataset from Horns Rev and tested for the downregulated period. The re-calibrated model has to be further parametrized to include dynamic effects such as wind direction variability and meandering also considering different averaging time scales before implemented in full scale wind farms.

Model of the Correlation between Lidar Systems and Wind Turbines for Lidar-Assisted Control

Abstract Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.

Editorial To Tame the Wind: Advanced Control Applications in Wind Energy

The aim of this special section is to illustrate the potential of advanced control methods to improve wind power technologies, through a collection of contributions concerned with several aspects including mitigation of tower vibration and fatigue loads, wind preview control, estimation of rotor effective wind speed, identification of rotor dynamics, fault tolerant control, wind farm control, and airborne wind energy. A significant number of contributions in the section are concerned with mitigation of vibrations and load reduction of wind turbines, in different frameworks and with different actuators and control approaches.

Estimation of Rotor Effective Wind Speed: A Comparison

Modern wind turbine controllers use wind speed information to improve power production and reduce loads on the turbine components. The turbine top wind speed measurement is unfortunately imprecise and not a good representative of the rotor effective wind speed. Consequently, many different model-based algorithms have been proposed that are able to estimate the wind speed using common turbine measurements. In this paper, we present a concise yet comprehensive analysis and comparison of these techniques, reviewing their advantages and drawbacks. We implement these techniques and compare the results on both aero-servo-elastic turbine simulations and real turbine field experiments in different wind scenarios.