Effective Wind Speed Estimation Research Articles

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.

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

FLORIDyn - A dynamic and flexible framework for real-time wind farm control

This paper presents a new framework of the FLOw Redirection and Induction Dynamics (FLORIDyn) model. It is able to dynamically simulate the wake behaviour in wind farms under heterogeneous and changing environmental conditions at a low computational cost. The novelty of this work is the improved segregation of wake dynamics and wake influence: the framework creates Observation Points (OPs) at each turbine, which propagate wind field states and turbine states downstream and follow the wind direction of the free stream velocity. These observation points cover the dynamic aspects of the simulation. The OPs, along with the stored states, are now used to derive so-called Temporary Wind Farms (TWF), which approximate the effective intra-farm wind conditions at a given location. Within these TWF, the flow conditions are homogeneous and steady state. This way, arbitrary wake models can be used to calculate the farm influence on the location. The FLORIDyn framework also provides interfaces to flow field estimators, which is tested with an effective wind speed estimator. A nine turbine case is used to highlight the quality and performance of the simulation result. Compared to its predecessor, the new FLORIDyn framework decreases the computational cost by one to two orders of magnitude, which makes it a promising candidate for real-time model predictive dynamic wind farm control.

Effective wind speed estimation study of the wind turbine based on deep learning

Wind speed is the driver of wind turbines, and the precise estimate of that makes it possible to improve the control effects and the efficiency of energy production. This paper proposes a method of effective wind speed estimator that considers the variation in blade radius and reconstructs the mapping of the aerodynamic. First, the proposed method utilizes available data of the wind turbine to train two neural network models based on radial basis functions (RBF). The models estimate the effective radius and reconstruct the aerodynamic mapping surface, respectively. Then, on this basis, train a wind speed estimation model based on the Long Short-Term Memory (LSTM) neural network, which can effectively estimate the current wind speed in real-time and predict the wind speed of the next time step as the reference for the following estimation. In addition, the RBF model and LSTM model can be updated and improved adaptively based on new data to ensure the accuracy of an effective wind speed estimator. Finally, the proposed wind speed estimation method is compared with the existing methods. The experimental results show that the proposed method has strong anti-interference characteristics, and improves the effective wind speed estimation accuracy over 70% on average.

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.

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 .

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.

Effective wind speed estimation based on a data-driven model of wind turbine tower deflection

The deflection of the wind turbine tower can provide us with rich information about the effective wind speed. In this paper, a new method for effective wind speed estimation is proposed based on tower deflection. The tower vibration model is derived and a subspace identification method is used to identify the model parameters. An online estimator of aerodynamic thrust force based on the identified tower model is designed and then implemented using a Kalman filter together with a recursive least squares algorithm. The estimated aerodynamic thrust force is then used as an input to a neural network estimator, which is trained to invert the aerodynamic thrust force equation and estimate the effective wind speed. In order to show the performance of the proposed estimator, the estimated thrust force and wind speed are compared and verified with a third-party simulation data of a 1.5 MW wind turbine. The comparison shows close agreement between their values.

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.

Modelling, Parametrization and Observer Design of a 20 MW Reference Wind Turbine for Control Purposes

In the present contribution, the model development of a wind turbine with focus on an advanced control system design is reported. The aim is to produce a model that captures essential dynamics over the simplest cases but maintaining an acceptable level of complexity. The problem of parameter reduction from high-resolution models, as used in the simulation of reference turbines, to models with low number of parameters, as normally used for control purposes, is also studied. The obtained parametric model is validated by developing an observer, which can be used for state-space control as well as effective wind speed estimation. As numerical example, a 20 MW reference wind turbine is used. Very large wind turbines are very flexible and nonlinear, therefore very simple rigid models do not lead to satisfactory results. The proposed observer is adjusted during operation in order to overcome this problem.

An Effective Wind Speed Estimation Based Extended Optimal Torque Control for Maximum Wind Energy Capture

Most variable-speed wind turbines employ advanced control scheme to improve their performance. In this paper, an extended optimal torque controller is designed based on effective wind speed estimation to control the variable-speed wind turbine. To do this, multilayer perceptron based nonlinear input-output mapping is firstly used for approximating the nonlinear aerodynamics of the wind turbine. In other words, based on this nonlinear mapping, effective wind speed is estimated from the measured rotor speed, the measured pitch angle, and the observed aerodynamic torque by the disturbance observer. After that, the optimal rotor speed command for capturing maximum wind energy is derived from the estimated effective wind speed. And then the optimal torque command is calculated by combing the standard optimal torque formula and a proportional control loop that is added to effectively reduce the moment of inertia. At last, some simulation results are validated to display the availability of the improved effective wind speed estimation algorithm and control strategy. Moreover, the corresponding simulation results indicate that compared with the existing methods, the proposed method increases the accuracy of the effective wind speed estimation by 2-7% and the energy production efficiency by 0.35%.

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.

Effective wind speed estimation for wind turbines in down-regulation

The use of state estimation technique offers a means of estimating the effective wind speed based solely upon the measurements of the turbine. Such wind speed estimations are crucial for turbines to calculate the reserved power for ancillary services. Nonetheless, the system observability or the possibility of estimating the wind speed is influenced by various down-regulation strategies. Thus, this paper investigates the system observabilities of turbines in different down-regulating conditions via the use of the empirical observability Gramian. In addition, wind speed estimators are constructed for each control strategy using an extended Kalman filter approach. Subsequently, each estimation performance is evaluated from the assessments of the Gramians and high-fidelity turbine simulations. The results reveal that it is more difficult to reconstruct the wind estimate from measurements of the turbine operating in torque-based than rotor-speed-based down-regulation strategies.

EWSE and Uncertainty and Disturbance Estimator Based Pitch Angle Control for Wind Turbine Systems Operating in Above-Rated Wind Speed Region

Abstract As wind energy becomes a larger part of the world's energy portfolio, the control of wind turbines is still confronted with challenges including wind speed randomness and high system uncertainties. In this study, a novel pitch angle controller based on effective wind speed estimation (EWSE) and uncertainty and disturbance estimator (UDE) is proposed for wind turbine systems (WTS) operating in above-rated wind speed region. The controller task is to maintain the WTS's generator power and rotor speed at their prescribed references, without measuring the wind speed information and accurate system model. This attempt also aims to bring a systematic solution to deal with different system characteristics over wide working range, including extreme and dynamic environmental conditions. First, support vector machine (SVR) based EWSE model is developed to estimate the effective wind speed in an online manner. Second, by integrating an UDE and EWSE model into the controller, highly turbulent and unpredictable dynamics introduced by wind speed and internal uncertainties is compensated. Rigid theoretical analysis guarantees the stability of the overall system. Finally, the performance of the novel pitch control scheme is testified via the professional Garrad Hassan (GH) bladed simulation platform with various working scenarios. The results reveal that the proposed approach achieves better performance in contrast to traditional L1 adaptive and proportional-integral (PI) pitch angle controllers.

Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine

Precise estimation of effective wind speed plays an important role in the advanced controls aiming at maximizing wind power extraction and reducing loads on turbine components. This paper proposes a sensorless effective wind speed estimation algorithm based on the unknown input disturbance observer and the extreme learning machine for the variable-speed wind turbine. First, aerodynamic torque is accurately estimated through an unknown input disturbance observer where the rotor speed is the measured output of the wind turbine drive train system. Then, the aerodynamic characteristics of the wind turbine are approximated by an extreme learning machine model based nonlinear input-output mapping. Last, effective wind speed is estimated based on the extreme learning machine model, using the previously estimated aerodynamic torque by the unknown input disturbance observer, together with the measured rotor speed and pitch angle. The proposed algorithm is validated by simulation studies on a 1.5 MW variable-speed wind turbine system. To evaluate the performance of the proposed algorithm, a detailed comparison with the Kalman filter-based method has been made. Comparison results clearly demonstrate that effective wind speed estimated by the proposed method is more accurate than that by the Kalman filter-based method and that the computational efficiency is higher.

Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine

The precise measurement of effective wind speed is a crucial task and has huge impact on wind turbine output power, safety and control performance. In this study, a hybrid intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed for online estimation of effective wind speed from instantaneous values of wind turbine tip speed ratio (TSR), rotor speed and mechanical power. The artificial neural network (ANN) adjusts the parameters of fuzzy membership functions (MFs) using hybrid optimization method. The estimated value of effective wind speed is further utilized to design the optimal rotor speed estimator for maximum power point tracking (MPPT) of variable-speed wind turbine (VSWT). Both estimators are implemented in MATLAB and their performance is investigated for national renewable energy laboratory (NREL) offshore 5 MW baseline wind turbine. The simulation results show the effectiveness of proposed method. The proposed scheme is computationally intelligent, easy to implement and more reliable for fast estimation of effective wind speed and optimal rotor speed.

Kalman filter-based wind speed estimation for wind turbine control

To improve power production and reduce loads on turbine components, exact wind speed information is required in modern wind turbine controllers. However, the wind speed measured on the nacelle is imprecise because of its drawbacks of single point measurement and non-immunity to disturbances. To solve this problem, the EWS (Effective Wind Speed) estimator has been proposed as an alternative. According to the literatures, there are two kinds of EWS estimator, that is, the KF-based estimator and the EKF-based one. Where, the former is applied to estimate the aerodynamic torque, then the EWS is numerically calculated; and the latter directly estimate the EWS. Since the estimate EWS significantly affect the controller’s effectiveness, their performance needs to be clarified. To fully investigate the two estimators, there is a need to evaluate their performance on an even platform. In this paper, we present comparative studies on these two methods. Their advantages and drawbacks are investigated on the commercial turbine design software-bladed and compared through detailed simulation results. Finally, we demonstrate some simulation results and differences between the KF-based estimator and the EKF-based one.

A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics

Variable speed wind turbines (VSWTs) usually adopt a maximum power point tracking (MPPT) method to optimize energy capture performance. Nevertheless, obtained performance offered by different MPPT methods may be affected by the impact of wind turbine (WT)’s inertia and wind speed characteristics and it needs to be clarified. In this paper, the tip speed ratio (TSR) and optimal torque (OT) methods are investigated in terms of their performance under different wind speed characteristics on a 1.5 MW wind turbine model. To this end, the TSR control method based on an effective wind speed estimator and the OT control method are firstly presented. Then, their performance is investigated and compared through simulation test results under different wind speeds using Bladed software. Comparison results show that the TSR control method can capture slightly more wind energy at the cost of high component loads than the other one under all wind conditions. Furthermore, it is found that both control methods present similar trends of power reduction that is relevant to mean wind speed and turbulence intensity. From the obtained results, we demonstrate that, to further improve MPPT capability of large VSWTs, other advanced control methods using wind speed prediction information need to be addressed.

Control Strategy to Maximize Power Extraction in Wind Turbine

ABSTRACTThis article deals with nonlinear control of variable speed wind turbine (VSWT), where the dynamics of the wind turbine (WT) is obtained from a single mass model. The main objective of this work is to maximize the energy capture form the wind with reduced oscillation on the drive train. The generator torque is considered as the control input to the WT. In general the conventional control techniques such as Aerodynamic Torque Feed-Forward (ATF) and Indirect Speed Control (ISC) are unable to track the dynamic aspect of the WT. To overcome the above drawbacks the nonlinear controllers such Sliding Mode Controller (SMC) and SMC with integral action (ISMC) with the estimation of effective wind speed are proposed. The Modified Newton Raphson (MNR) is used to estimate the effective wind speed from aero dynamic torque and rotor speed. The proposed controller is tested with different wind profiles with the presence of disturbances and model uncertainty. From the results the proposed controller was found to...