Wind Speed Estimation Methods Research Articles

Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies

A main external factor that limits the energy assessment of high-rise buildings in urban areas is the vertical wind conditions. Conventional buildings' energy assessment tools apply a universal vertical wind profile estimation equation in all directions and only consider four typical terrain types. However, the various shape and configurations of buildings in cities can induce the unique wind speed in each direction, and the vertical wind speeds in the same terrain type can be different even in the same city. To get more realistic vertical wind conditions, this study developed an omnidirectional urban vertical wind speed estimation method with direction-dependent building morphologies. A pie-shaped segment method is applied to calculate building morphological features in approaching wind direction. Machine learning methods are developed to calculate parameters of the modified direction-dependent power law urban wind function to convert the Typical Meteorological Year (TMY) wind speed to urban vertical wind speeds. To validate the proposed method, wind tunnel data in Hong Kong are collected for a case study. The results indicate that the proposed method with morphological inputs can effectively estimate the vertical wind speed at different wind directions and heights, and deep neural networks and support vector regression have better performance.

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.

Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation

Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.

Low-Altitude Windshear Estimation Method Based on Four-Dimensional Frequency Domain Compensation for Fuselage Frustum Conformal Array

In this paper, a low-altitude wind speed estimation method based on the fuselage frustum conformal array system is proposed. Firstly, based on the signal model of the fuselage conformal array radar, the four-dimensional joint phase compensation of the echo data in the Doppler domain and three-dimensional space-frequency domain is performed by using the four-dimensional frequency domain compensation method. Secondly, the clutter covariance matrix is estimated by the compensated echo data, and a space-time Adaptive Processing (STAP) processor suitable for low-altitude windshear target is constructed to suppress clutter. Finally, the maximum Doppler value of each distance cell is extracted, and the wind velocity is estimated. Simulation results show that the proposed method can effectively suppress clutter and accurately estimate wind speed.

Low-Altitude Windshear Wind Speed Estimation Method Based on KASPICE-STAP

Aiming at the problem of low-altitude windshear wind speed estimation for airborne weather radar without independent identically distributed (IID) training samples, this paper proposes a low-altitude windshear wind speed estimation method based on knowledge-aided sparse iterative covariance-based estimation STAP (KASPICE-STAP). Firstly, a clutter dictionary composed of clutter space-time steering vectors is constructed using prior knowledge of the distribution position of ground clutter echo signals in the space-time spectrum. Secondly, the SPICE algorithm is used to obtain the clutter covariance matrix iteratively. Finally, the STAP processor is designed to eliminate the ground clutter echo signal, and the wind speed is estimated after eliminating the ground clutter echo signal. The simulation results show that the proposed method can accurately realize a low-altitude windshear wind speed estimation without IID training samples.

Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System

The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.

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.

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.

Machine Learning-Based Predictive the Forecasting of Short Term Wind Speed

As a possible emerging technology for electricity generation, wind power is rapidly evolving world’s mainstream influence. However, the inherent spontaneous wind instability poses great obstacles to stable grid usage and power supply stability. One way of efficiently addressing the wind instability problem is to enhance accurate wind velocity forecasts. However, the intrinsic regularity of wind speed data cannot be for the bulk of the wind speed estimation method. Therefore, this paper introduces machine learning (ML) based genetic algorithm (GA) and the Short-term Wind Speed Prediction Model, which can effectively improve wind speed prediction accuracy. The work of study will help evaluate the power grid risks adequately. The appropriate electricity system divisions are expected to create a realistic generation schedule, minimize cost effectively, and significantly encourage green-energy growth.

Modeling and simulation of UAV static soaring based on multi-hole probe

Unmanned Aerial Vehicles (UAVs) can extract energy to improve their range and endurance due to thermal updrafts in the environment via static soaring. In this paper, a soaring control strategy is improved based on multi-hole probe technology. A vertical wind speed estimation method based on the multi-hole probe is proposed to design thermal updraft identification, mode switching logic, and the soaring controller. Turn logic is used to determine the correct turn direction for UAVs when a thermal updraft is detected. A simulation environment containing a six-degree-of-freedom glider model and a thermal updraft model is described. The induced roll effect caused by the asymmetric vertical velocity distribution of thermal updraft is included in the simulations. The results show an improved soaring capability from the proposed soaring strategy. Thus, the proposed method based on a multi-hole probe is more accurate and robust, and the turn logic allows a glider to enter a thermal updraft more quickly. The more energy a glider can obtain from thermal updraft enables a lower soaring speed during static soaring.

Data-Based Windstorm Type Identification Algorithm and Extreme Wind Speed Prediction

AbstractThe extreme wind speed estimation method, which is critical for designing wind load calculation for building structures, should consider windstorm climate types for mixed climates. However,...

Development and performance analysis of intelligent fault ride through control scheme in the dynamic behaviour of grid connected DFIG based wind systems

As the penetration of wind power increases, the fault ride through requirements imposed by grid code is important in grid connected wind systems. The wind systems should remain connected to the grid during grid faults satisfying the grid code which ensures grid stability during fault and post fault conditions. This paper proposes an intelligent fault ride through strategy for Doubly Fed Induction Generators (DFIG) based Wind Energy Conversion Systems (WECS) to achieve real and reactive power control during grid faults. The transient behaviour of the system is investigated under normal conditions and during grid faults. A fuzzy based wind speed estimation method in Maximum Power Point Tracking (MPPT) mode under normal conditions and coordinated Genetic Algorithm based Real-Reactive (GA-PQ) controller with DC chopper in Fault Ride Through (FRT) mode during grid faults is developed in this work. The proposed control scheme provides smooth operation of DFIG during grid faults by controlling the rotor and grid side converters, providing reactive power support to the grid and relieving stress on power converters thereby achieving system stability. The proposed strategy maintains the system parameters during the grid faults by suppressing the rotor and stator over current, dc link voltage overshoot, power oscillations and support the grid voltage under both balanced and unbalanced grid fault conditions with different voltage dips at PCC. Computer simulations in time-domain are performed by verifying the MPPT and FRT capability of the proposed strategy for 6.5MW grid connected DFIG based WECS in MATLAB/SIMULINK 2018b. The proposed coordinated intelligent PQ and DC chopper FRT strategy is compared against existing FRT schemes like crowbar, SDBR, DC Chopper, PQ controller and its hybrid combinations which yields satisfactory results. The proposed scheme is also validated in Opal-RT hardware for LLG fault at 75% dip in voltage. The simulation and real time results observed in the work station demonstrates the effectiveness of the proposed strategy in enhancing the FRT capability of the system.

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.

Critical wind speeds suggest wind could be an important disturbance agent in Amazonian forests

Abstract Recent research in the central Amazon suggests that wind is a major agent of disturbance, however, a mechanistic understanding of how wind may lead to tree mortality in Amazonian forests remains unclear. Here we estimated wind speeds necessary to topple central Amazon trees by linking both static and dynamic versions of two wind speed estimation methods (four methods total) to field data on tree failure derived from a static winching study. Static versions of these methods assumed invariant wind characteristics as more trees failed, while dynamic versions updated tree spacing, leaf area index and wind profiles progressively after each tree failure. First, we used a profile method which estimates wind force on individual trees by segments. We calculated drag on each segment and converted drag into basal turning moment, and compared the summed turning moments to the critical turning moment measured in the winching study. Estimated critical wind speeds from the static profile method varied greatly, from 10.75 m s−1 to >120.0 m s−1 with a mean of 45.70 m s−1. Critical wind speeds estimated with static approaches decreased with tree size but were not significantly different between two focal genera. Primary drivers of variation in critical wind speed were tree height and crown size. Second, we used the turning moment coefficient method of Hale, S.E., Gardiner, B., Peace, A., Nicoll, B., Taylor, P. and Pizzirani, S. 2015 Comparison and validation of three versions of a forest wind risk model. Environ. Model. Softw.68, 27–41. doi:10.1016/j.envsoft.2015.01.016.; the static version of this method yielded less-variable estimates, ranging from 18.98 to 52.01 m s−1, with a mean of 30.88 m s−1. Notably, the two static methods for estimating critical wind speeds differed in the trees they identified as having the highest and lowest critical wind speeds. Dynamic variants of the above two methods produced greatly reduced ranges in CWS estimates for our study trees, because after the early tree failures, remaining trees were subject to greater wind penetration into the stand and thus greater loading for a given above-canopy wind speed. CWS estimated with dynamic approaches differed significantly between the focal taxa. Nevertheless, both estimates suggest that wind speeds commonly observed during Amazon storms are sufficient to produce widespread tree damage and mortality.

NARX-Network Based Wind Speed Estimation for Wind Turbines

This paper proposes a new wind speed estimation method for the control systems of wind turbines. The nonlinear autoregressive network with exogenous inputs (NARX) is applied to model the wind turbine and estimate the wind speed. The actual data from a wind farm is used to train both the traditional multilayer perceptron neural network (MLP) and the NARX-network. The experimental results show that NARX-network can produce a more accurate estimation than the traditional multilayer perceptron.

A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique

This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbine systems are reviewed.

A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique

This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbine systems are reviewed.

Wind turbine wake visualization and characteristics analysis by Doppler lidar

Wind power generation is growing fast as one of the most promising renewable energy sources that can serve as an alternative to fossil fuel-generated electricity. When the wind turbine generator (WTG) extracts power from the wind, the wake evolves and leads to a considerable reduction in the efficiency of the actual power generation. Furthermore, the wake effect can lead to the increase of turbulence induced fatigue loads that reduce the life time of WTGs. In this work, a pulsed coherent Doppler lidar (PCDL) has been developed and deployed to visualize wind turbine wakes and to characterize the geometry and dynamics of wakes. As compared with the commercial off-the-shelf coherent lidars, the PCDL in this work has higher updating rate of 4 Hz and variable physical spatial resolution from 15 to 60 m, which improves its capability to observation the instantaneous turbulent wind field. The wind speed estimation method from the arc scan technique was evaluated in comparison with wind mast measurements. Field experiments were performed to study the turbulent wind field in the vicinity of operating WTGs in the onshore and offshore wind parks from 2013 to 2015. Techniques based on a single and a dual Doppler lidar were employed for elucidating main features of turbine wakes, including wind velocity deficit, wake dimension, velocity profile, 2D wind vector with resolution of 10 m, turbulence dissipation rate and turbulence intensity under different conditions of surface roughness. The paper shows that the PCDL is a practical tool for wind energy research and will provide a significant basis for wind farm site selection, design and optimization.

Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system

This paper proposes a precise real-time wind speed estimation method and sensorless control for variable-speed variable-pitch wind turbine power generation system (WTPGS). The wind speed estimation is realized by a nonlinear input–output mapping extreme learning machine (ELM). A specific design characteristic of the wind turbine is used for improving the mapping accuracy with considering the variable pitch angle. Moreover, since the design is independent of the environmental air density, the proposed ELM wind speed estimation method is robust to the air density variations. The estimated wind speed is then used to determine the optimal rotational speed command for maximum power point tracking. A fast and effective ELM mapping based pitch controller is proposed too when the WTPGS operates in its high wind speed region. The ELM pitch controller can act much faster and more precise than conventional pitch controllers. Furthermore, the complicated design precess for the parameters of conventional pitch controllers will be avoided in the proposed method. The effectiveness of the proposed methods are verified both by simulations and experiments on a WTPGS installed with Permanent Magnet Synchronous Generator (PMSG).