Rotor Effective Wind Speed Research Articles

Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar

Abstract. Feedforward blade pitch control is one of the most promising lidar-assisted control strategies due to its significant improvement in rotor speed regulation and fatigue load reduction. A high-quality preview of the rotor-effective wind speed is a key element of control benefits. In this work, a single-beam lidar is simulated in the spinner of a bottom-fixed IEA 15 MW wind turbine. Both continuous-wave (CW) and pulsed lidar systems are considered. The single-beam lidar can rotate with the wind turbine rotor and scan the inflow with a circular pattern, which mimics a multiple-beam nacelle lidar at a lower cost. Also, the spinner-based lidar has an unimpeded view of the inflow without intermittent blockage from the rotating blade. The focus distance and the cone angle of the spinner-based single-beam lidar are optimized for the best wind preview quality based on a rotor-effective wind speed coherence model. Then, the control benefits of using the optimized spinner-based lidar are evaluated for an above-rated wind speed in OpenFAST with an embedded lidar simulator and virtual four-dimensional Mann turbulence fields considering the wind evolution. Results are compared against those using a single-beam nacelle-based lidar. We found that the optimum scanning configurations of both CW and pulsed spinner-based single-beam lidars lead to a lidar scan radius of 0.6 of the rotor radius. Also, results show that a single-beam lidar mounted in the spinner provides many more control benefits (i.e. better rotor speed regulations and higher reductions in the damage equivalent loads on the tower base and blade roots) than the one based on the nacelle. The spinner-based single-beam lidar has a similar performance to a four-beam nacelle lidar when used for feedforward control.

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimations

Lidar simulation techniques are a suitable and increasingly reliable alternative for testing lidar measuring strategies and illustrating their response when combined with modelled wind fields. In this work, two simulation tools are combined to assess the uncertainty in the derivation of the rotor effective wind speed and the wind speed variance from a forward-looking nacelle-mounted continuous wave lidar wind speed estimations. These uncertainties are analysed for a variety of atmospheric turbulence levels and lidar measuring strategies. A synthetic turbulence generator is used to create the reference wind fields. Subsequently, a lidar simulator operated in a continuous-wave mode is used to scan the synthetic wind fields and perform a sensitivity analysis by comparing first- and second-order statistics against reference values. The lidar simulator is enhanced with three Doppler peak detection methods, namely the maximum, the median and the centroid, to extract radial wind speeds from the velocities found within the probe volume. The results show that probe volume and peak detection methods influence the uncertainty of the wind speed variance. The uncertainty in time-averaged and instantaneous rotor effective wind speed estimations is not sensitive to the lidar spatial averaging or peak detection methods investigated. Finally, we saw that the turbulence intensity influences the derived lidar quantities and is the main driver of the variations in rotor effective wind speed uncertainty estimations.

Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation

AbstractThe use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre‐computed quasi‐steady aerodynamic mapping, which describes the relationship between pitch angle and tip‐speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterized in terms of axial induction factors. Based on this insight and standard turbine measurements, real‐time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine drive‐train model and an extended Kalman filter. In addition, the actuator disc model can be updated easily over time by calibrating solely two correction factors. Thus, the proposed algorithm presents an alternative for estimating the rotor effective wind speed, which is valuable for numerous applications, for example, LiDAR‐assisted control and coherence studies.

An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Wind field reconstruction using nacelle based lidar measurements for floating wind turbines

In this work we investigate the influence of floater motions on nacelle based lidar wind speed measurements. The analysis is focused on wind field characteristics which are most relevant for performance analysis, namely rotor effective wind speed, shear and turbulence intensity. A numerical approach, coupling the publicly available in-house lidar simulation framework ViConDAR (Virtual Constrained turbulence and liDAR measurements) with an aeroelastic floating offshore wind turbine simulation is employed. Synthetic turbulent wind fields are generated with the open source turbulence generator TurbSim. The dynamics of the floating offshore wind turbine are simulated in the aeroelastic simulation code OpenFAST. Turbine dynamics and the corresponding synthetic wind field are then passed to the lidar simulation module of ViConDAR, which has been adapted for the consideration of turbine dynamics in all six degrees of freedom to simulate the lidar measurements under influence of motion. The simulation framework is demonstrated in a case study, simulating a lidar system on the nacelle of the IEA 15 MW turbine in combination with the WindCrete floater concept. Two different realistic lidar patterns are investigated under different metocean conditions. Different motion cases are examined individually and combined to evaluate the influence of rotational and translational degrees of freedom. Results show an increase of mean absolute error between lidar estimated and full wind field rotor effective wind speed of 0 to 25% depending on the environmental conditions. Observed overestimation of mean rotor effective wind speed was found to be in the region of up to 1%.

Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions

This paper presents an analysis of the power loss of the upstream turbine for yaw misalignment depending on the inflow condition. High-quality experimental field data, obtained from a lidar wind profiler, met mast, GPS based yaw sensor, turbine data, and load data is used. To reduce the spreading of the power loss during yaw misalignment, the rotor effective wind speed is estimated using load data. Furthermore, the so-called power loss coefficient currently used in most engineering models, for yaw misalignment, is assessed at various inflow conditions. As main finding, the power loss coefficient increases for stronger vertical wind shear. In addition, there is an asymmetry with respect to the direction of the yaw misalignment that is enhanced by the wind shear exponent and the wind veer. Neglecting these two effects could result in in-conservative wake-steering decisions at stable stratification

Wind turbine LIDAR-assisted control: Power improvement, wind coherence and loads reduction

This paper aims to firstly review various LIDAR-assisted wind turbine control methods proposed in the past ten years for improving power production in the below rated wind speed region in order to clarify their performance and quantify the potential benefits and drawbacks of LIDAR-assisted control. Secondly, a new LIDAR-assisted control algorithm based on Extremum Seeking Control (ESC) using LIDAR preview information for tracking the optimal power coefficient value is presented and compared to the traditional control strategy. The results show that the newly proposed LIDAR-assisted control strategy is beneficial in the below rated wind speed region when the aerodynamic properties of blades are changed largely compared to the original design. Lastly, the effects of the wind field coherence between the LIDAR measured points and the rotor plane on the LIDAR-assisted control for loads reduction in above rated region is discussed. An important conclusion is that the major benefit of LIDAR-assisted wind turbine control is to improve collective pitch control for reducing fatigue loads in above rated region, when the coherence bandwidth between the LIDAR measured wind speed and the rotor effective wind speed is high.

WITHDRAWN: Nonlinear Kalman filters based rotor effective wind speed estimation: An experimental comparison

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy .

The Immersion and Invariance Wind Speed Estimator Revisited and New Results

The Immersion and Invariance (I&I) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway, its global convergence proof is rather cumbersome, which hinders the extension of the method and proof to time-delayed and/or uncertain systems. In this letter, we illustrate that the circle criterion can be used as an alternative method to prove the global convergence of the I&I estimator. This also opens up the inclusion of time-delays and uncertainties. First, we demonstrate that the I&I wind speed estimator is equivalent to a torque balance estimator with a proportional correction term. As the nonlinearity in the estimator is sector bounded, the well-known circle criterion is applied to the estimator to guarantee its global convergence for time-delayed systems. By looking at the theoretical framework from this new perspective, this letter further proposes the addition of an integrator to the correction term to improve the estimator performance. Case studies show that the proposed estimator with an additional integral correction term is effective at wind speed estimation. Furthermore, its global convergence can be guaranteed by the circle criterion for time-delayed systems.

The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the Blade Effective Wind Speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: A Proportional Integral Notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the non-rotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well behaved transient response than the PIN estimator.

On turbulence models and lidar measurements for wind turbine control

Abstract. To provide comprehensive information that will assist in making decisions regarding the adoption of lidar-assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor-effective wind speed and lidar measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of lidar systems are examined to analyze the lidar measurement coherence based on commercially available lidar scan patterns. Finally, numerical simulations have been performed to compare the lidar measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the lidar measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the potential value creation of LAC based on simulations during the wind turbine design phase, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered uncertainties while evaluating the benefits of LAC.

Novel rotor effective wind speed estimation method for light detection and ranging application

The ‘Cyclops’ effect of the light detection and ranging (LiDAR) system means that the LiDAR system can only measure wind speed at one point in space at a certain moment. However, for the purposes of wind turbine control, considering the wind information at the rotor plane with time stamps can be more precise. The wind speed attenuates due to the induction zone; however, Taylor's frozen hypothesis is commonly used in the application of LiDAR for the rotor effective wind speed estimation. The wind turbine is controlled directly by LiDAR measured data with constant time delay, which weakens LiDAR's effectiveness. In this paper, a novel LiDAR data pre-processing method is demonstrated. The Medici induction zone model is further refined with initial wind speed and different measured distances for accurate estimation. Furthermore, it makes online variable time delay of LiDAR data computation possible with the solution of the separated differential equation. Finally, the novel method is fully verified through field test results. The results show that the initial wind speed affected the attenuation factor within 1.4 times rotor radius and the error between the experimental value and the theoretical value is within 0.1 m/s.

Turbine layout optimisation for large‐scale offshore wind farms–A grid‐based method

This study presents a new turbine layout optimisation approach using a grid-based problem formulation for improved design performance and computational efficiency for industrial-scale applications. A particle swarm optimisation algorithm is employed in the wind turbine layout optimisation, in which a micro-siting function is proposed to allow solutions 50 m of deviation while maximising energy capture without compromising maritime navigation or search and rescue operations. Solutions are assessed by a wind farm model, comprising the Larsen wake model, a multiple wake effect summation method, and a rotor-effective wind speed calculation. A novel look-up function is populated by on-the-fly algorithm and is used to reduce the number of model evaluations by approximately 95%. A gigawatt scale hypothetical site is presented to test the model on a realistically complex scenario. A set of design solutions generated by the algorithm are compared to empirical designs, with the algorithm outperforming the empirical solutions by 7.55% on average, in terms of net-present-value of energy capture minus the capital cost of turbines. The numerical efficiency and design effectiveness are examined and further improvements discussed.

Analysis and design of an adaptive turbulence-based controller for wind turbines

This work aims to explore methods to retain the robustness and performance of a wind turbine controller under different wind conditions. A method of optimizing the control parameters in response to different turbulence intensity is proposed, which is referred to as adaptive turbulence-based control (ATBC). Specifically, the power spectrum of the rotor effective wind speed has been derived and the analytical expression is explicitly considered in the control optimization. Also, a linear aero-servo-elastic (ASE) model is established, which captures the closed-loop dynamics of the rotor speed, pitch activity and tower fore-aft vibration mode. Subsequently, a computationally-efficient component damage prediction method is proposed that uses rainflow counting and inverse fast Fourier transform. Based on the proposed ASE model and damage prediction method, the controller optimization problem is established using a quadratic cost function to achieve the optimal trade-off between the rotor speed variation and the damage of turbine components. A model validation shows that the proposed scheme is able to predict the component fatigue load and the rotor speed variation in an efficient way. Finally, one design case is given to illustrate the procedure of ATBC and to demonstrate the feasibility of the proposed method in different operating wind conditions.

Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances

Model predictive control of a floating offshore wind turbine-generator system, in which wave height as well as inflow wind speed is regarded as the previewed disturbances, is developed to stabilize power output and platform motion and reduce dynamic loads at mechanical and supporting components at high wind speeds. First, the internal model to predict dynamic control behaviors to previewed disturbances is identified from an aero-elastic-hydro-control coupled simulation result, in which pseudorandom binary sequence signals are added to the manipulated variables calculated in a gain-scheduling feedback controller of the generator speed to satisfy a persistently exciting condition. Second, an aero-elastic-hydro-control coupled simulation using the developed model predictive control is performed for a 5-MW floating offshore wind turbine-generator system. The identified internal model has a high prediction accuracy of the system outputs by regarding the spatial mean wind speed in the swept area of the wind turbine as a rotor effective wind speed. The simulation results under turbulent wind fields and irregular wave height variations reveal that the stabilization of the power output and platform motion and the dynamic load reduction are achieved by employing the developed model predictive control with a perfect preview of the wind speed and wave height.

Real-time rotor effective wind speed estimation using Gaussian process regression and Kalman filtering

The use of state estimation technique offers a means of inferring the rotor effective wind speed based upon solely standard measurements of wind turbines. For the ease of design and computational concerns, typical wind speed estimators rely on a pre-computed mapping that describes the relationship from tip-speed ratio and pitch angle to the power coefficient. Typically, this mapping is built using numerical simulations under steady inflow conditions. Thus, the mapping built by traditional methods does not well capture the influence of other turbine dynamics and atmospheric variations, thus, inevitably resulting in poor performance of the wind speed estimator. Therefore, the paper presents a framework of rotor effective wind speed estimator design that obviates the need for a pre-computed power coefficient mapping. Specifically, the proposed method reconstructs the mapping using Gaussian process regression with a small set of real-time turbine measurement data. Subsequently, the wind speed estimator is built based upon the regression-based model and an extended Kalman filter, enabling optimal estimation from standard turbine measurements. The proposed method was evaluated in normal operation and down-regulation, against benchmark models obtained from an aero-elastic code. The estimation errors of the power coefficient and wind speed were significantly reduced by the regression-based approach.

Kalman-based interacting multiple-model wind speed estimator for wind turbines

The use of state estimation technique offers a means of inferring the rotor-effective wind speed based upon solely standard measurements of the turbine. For the ease of design and computational concerns, such estimators are typically built based upon simplified turbine models that characterise the turbine with rigid blades. Large model mismatch, particularly in the power coefficient, could lead to degradation in estimation performance. Therefore, in order to effectively reduce the adverse impact of parameter uncertainties in the estimator model, this paper develops a wind sped estimator based on the concept of interacting multiple-model adaptive estimation. The proposed estimator is composed of a bank of extended Kalman filters and each filter model is developed based on different power coefficient mapping to match the operating turbine parameter. Subsequently, the algorithm combines the wind speed estimates provided by each filter based on their statistical properties. In addition, the proposed estimator not only can infer the rotor-effective wind speed, but also the uncertain system parameters, namely, the power coefficient. Simulation results demonstrate the proposed estimator achieved better improvement in estimating the rotor-effective wind speed and power coefficient compared to the standard Kalman filter approach.

Adaptive Dual-layer Sliding Mode Control for Wind Turbines with Estimated Wind Speed

Under the circumstance of inaccurate and intermittent wind speed measurement, maximum power capture control of wind turbines is still a hot and challenging topic in wind power generation field. This paper aims at improving wind turbines’ power production via an adaptive dual-layer sliding mode controller, with the help of estimated rotor effective wind speed. First, a novel effective wind speed estimation algorithm is proposed based on broad learning system (BLS), the training of which is completed by using data collected from the supervisory control and data acquisition (SCADA) system. Further, the trained BLS can deliver the estimated wind speed in an online manner to determine the optimal generator power command for maximum wind power extraction. In addition, a low-pass filter is designed to smooth the output, which is beneficial for drive train systems’ mechanical loads. Thereafter, a sliding mode control theory based maximum power point tracking (MPPT) controller is developed. To compensate for the uncertainties and mitigate the chattering phenomena, dual-layer adaptation laws are designed for the sliding mode controller. Finally, the effectiveness of the proposed control scheme is validated and demonstrated by the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool.

Lidar estimation of rotor-effective wind speed – an experimental comparison

Abstract. Lidar systems have the potential of alleviating structural loads on wind turbines by providing a preview of the incoming wind field to the control system. For a collective pitch controller, the important quantity of interest is the rotor-effective wind speed (REWS). In this study, we present a model of the coherence between the REWS and its estimate from continuous-wave nacelle-mounted lidar systems. The model uses the spectral tensor definition of the Mann model. Model results were compared to field data gathered from a two- and four-beam nacelle lidar mounted on a wind turbine. The comparison shows close agreement for the coherence, and the data fit better to the proposed model than to a model based on the Kaimal turbulence model, which underestimates the coherence. Inflow conditions with larger length scales led to a higher coherence between REWS and lidar estimates than inflow turbulence of smaller length scale. When comparing the two lidar systems, it was shown that the four-beam lidar is able to resolve small turbulent structures with a higher degree of coherence. Further, the advection speed by which the turbulent structures are transported from measurement to rotor plane can be estimated by 10 min averages of the lidar estimation of REWS. The presented model can be used as a computationally efficient tool to optimize the position of the lidar focus points in order to maximize the coherence.