Spatiotemporal variability of the wind power resource in Argentina and Uruguay

The spatiotemporal variability of the wind power resource over Argentina and Uruguay is assessed based on the Modern‐Era Retrospective Analysis for Research and Applications 2 (MERRA2) dataset. Hourly wind speeds were interpolated to 100‐m height, and then, wind power outputs were computed using power curves of three International Electrotechnical Commission (IEC) wind classes. The time series of wind power outputs were filtered using a fast Fourier transform (FFT) to separate regular (annual and daily) from irregular (interannual and synoptic scale) cycles. An empirical orthogonal function analysis was applied to the resulting datasets to obtain the main modes of variability. The results show that the combination of wind power outputs from southern and northern Patagonia broadly follows the average annual electric load. Patagonia exhibits the highest variability on the interannual, annual, and synoptic timescales. On the interannual and synoptic timescales, the variability modes are associated with known and distinct atmospheric circulation modes. The interannual modes of variability are associated with opposite surface level pressure (SLP) anomalies between middle and high latitudes.     

Can synoptic drivers explain rotor-area wind conditions and predict energy output?

Synoptic-scale weather patterns are an important driver of wind speed at turbine hub height, but wind energy generation is also affected by the wind profile across the rotor. In this research, we use a 6-year record of hourly profile measurements at the Eolos Wind Research Station in Minnesota, USA, to investigate whether synoptic weather patterns can provide information about rotor-area characteristics in addition to hub-height wind speed. We use sea level pressure data from the MERRA-2 reanalysis to classify synoptic patterns at the Eolos site into 15 synoptic types and use the Eolos wind profile data to create mean hourly and mean monthly values of wind speed and turbulence intensity at hub height (80 m), and wind speed shear, wind direction shear, and the potential temperature gradient across the rotor (30–129 m), for each synoptic type. Using a simple linear regression model, we find that, at monthly time scales, wind speed, turbulence intensity, and wind speed shear across the rotor are the most important variables for predicting monthly wind energy output from the Eolos turbine. Regression models using the original Eolos data and the derived synoptic types capture about 64% and 55% of the variance in monthly energy output, respectively. When fewer than the full 6 years of observations are used to fit the regression model, however, predictions using the synoptic types slightly outperform predictions using the Eolos observations. These results suggest that seasonal energy projections may be enhanced by incorporating wind profile measurements with synoptic-scale drivers.     

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency‐domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportional‐integral‐derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta‐heuristic algorithms.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Wind climate from the regional climate model REMO

Selected outputs from simulations with the regional climate model REMO from the Max Planck Institute, Hamburg, Germany were studied in connection with wind energy resource assessment. It was found that the mean wind characteristics based on observations from six mid‐latitude stations are well described by the standard winds derived from the REMO pressure data. The mean wind parameters include the directional wind distribution, directional and omni‐directional mean values and Weibull fitting parameters, spectral analysis and interannual variability of the standard winds. It was also found that, on average, the wind characteristics from REMO are in better agreement with observations than those derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re‐analysis pressure data. The spatial correlation of REMO surface winds in Europe is consistent with that of the NCEP/NCAR surface winds, as well as published observations over Europe at synoptic scales. Therefore, REMO outputs are well suited for wind energy assessment application in Northern Europe. Copyright © 2009 John Wiley & Sons, Ltd.     

Short‐term forecasting of surface layer wind speed using a continuous random cascade model

This paper describes a statistical method for short‐term forecasting (1–12 h ahead) of surface layer wind speed using only recent observations, relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a ‘multifractal noise’ associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and the Netherlands. The obtained results show that a better modeling of the noise term based on cascade process enhances the forecast; furthermore, there is a systematic improvement in the prediction as compared with reference models. Copyright © 2011 John Wiley & Sons, Ltd.     

Wind power forecast using neural networks: Tuning with optimization techniques and error analysis

The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short‐term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg‐Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN‐LM wind speed forecasting model, outperforming both ANN‐PSO and persistence. The application of ANN‐LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper.     

Numerical investigation of unsteady aerodynamics of a Horizontal‐axis wind turbine under yawed flow conditions

In the present study, unsteady flow features and the blade aerodynamic loading of the National Renewable Energy Laboratory phase VI wind turbine rotor, under yawed flow conditions, were numerically investigated by using a three‐dimensional incompressible flow solver based on unstructured overset meshes. The effect of turbulence, including laminar‐turbulent transition, was accounted for by using a correlation‐based transition turbulence model. The calculations were made for an upwind configuration at wind speeds of 7, 10 and 15 m/sec when the turbine rotor was at 30° and 60° yaw angles. The results were compared with measurements in terms of the blade surface pressure and the normal and tangential forces at selected blade radial locations. It was found that under the yawed flow conditions, the blade aerodynamic loading is significantly reduced. Also, because of the wind velocity component aligned tangent to the rotor disk plane, the periodic fluctuation of blade loading is obtained with lower magnitudes at the advancing blade side and higher magnitudes at the retreating side. This tendency is further magnified as the yaw angle becomes larger. At 7 m/sec wind speed, the sectional angle of attack is relatively small, and the flow remains mostly attached to the blade surface. At 10 m/sec wind speed, leading‐edge flow separation and strong radial flow are observed at the inboard portion of the retreating blade. As the wind speed is further increased, the flow separation and the radial flow become more pronounced. It was demonstrated that these highly unsteady three‐dimensional aerodynamic features are well‐captured by the present method. Copyright © 2012 John Wiley & Sons, Ltd.     

Extreme winds from the NCEP/NCAR reanalysis data

In this study, we test a method to estimate the extreme winds by using the NCEP/NCAR reanalysis data. From the reanalysis pressure or geopotential height records, the geostrophic wind is first calculated, and then extrapolated to 10 m height over a homogeneous surface with roughness length of 0.05 m, i.e. the so‐called standard wind. The software Wind Analysis and Application Program will then use this standard wind in a flow model, with the roughness, orography and obstacles around the turbine site to obtain the site‐specific wind. The ‘annual maximum method’ is used to calculate the 50 year wind. We examined extreme winds in different places where the strongest wind events are weather phenomena of different scales, including the mid‐latitude lows in Denmark, channelling winds in the Gulf of Suez, typhoons in the western North Pacific, cyclones in the Caribbean Sea, local strong winds: the Mistral in the Gulf of Lions and the Bora in the north Adriatic Sea. It was found that the method introduced here can be applied to places where the extreme wind events are synoptic weather phenomena like in north‐western Europe, but a more complicated downscaling, e.g. based on a mesoscale model, is needed for places where the extreme wind events are of mesoscale origin. Copyright © 2009 John Wiley & Sons, Ltd.     

Wind shear coefficients and their effect on energy production

This paper provides realistic values of wind shear coefficients calculated using measured values of wind speed at 20, 30 and 40 m above the ground for the first time in Saudi Arabia in particular and, to the best of the authors’ knowledge, in the Gulf region in general. The paper also presents air density values calculated using the measured air temperature and surface pressure and the effects of wind shear factor on energy production from wind machines of different sizes. The measured data used in the study covered a period of almost three years between June 17, 1995 and December 1998. An overall mean value of wind shear coefficient of 0.194 can be used with confidence to calculate the wind speed at different heights if measured values are known at one height. The study showed that the wind shear coefficient is significantly influenced by seasonal and diurnal changes. Hence, for precise estimations of wind speed at a height, both monthly or seasonal and hourly or night time and day time average values of wind shear coefficient must be used. It is suggested that the wind shear coefficients must be calculated either (i) using long term average values of wind speed at different heights or (ii) using those half hourly mean values of wind speed for which the wind shear coefficient lies in the range ⩾0 and ⩽0.51. The air density, calculated using measured temperature and pressure was found to be 1.18 kg/m³. The air density values were also found to vary with the season of the year and hour of the day, and hence, care must be taken when precise calculations are to be made. The air density values, as shown in this paper, have no significant variation with height. The energy production analysis showed that the actual wind shear coefficient presented in this paper produced 6% more energy compared to that obtained using the 1/7 power law. Similarly, higher plant capacity factors were obtained with the wind shear factor of 0.194 compared to that with 0.143.     

Improved synthetic wind speed generation using modified Mycielski approach

In this paper, novel approaches for wind speed data generation using Mycielski algorithm are developed and presented. To show the accuracy of developed approaches, we used three‐year collected wind speed data belonging to deliberately selected two different regions of Turkey (Izmir and Kayseri) to generate artificial wind speed data. The data belonging to the first two years are used for training, whereas the remaining one‐year data are used for testing and accuracy comparison purposes. The concept of distinct synthetic data production with correlation‐wise and distribution‐wise similar statistical properties constitutes the main idea of the proposed methods for a successful artificial wind speed generation. Generated data are compared with test data for both regions in the sense of basic statistics, Weibull distribution parameters, transition probabilities, spectral densities, and autocorrelation functions; and are also compared with the data generated by the classical first‐order Markov chains method. Results indicate that the accuracy and realistic behavior of the proposed method is superior to the classical method in the literature. Comparisons and results are discussed in detail. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal blade pitch control with realistic preview wind measurements

In above‐rated wind speeds, the goal of a wind turbine blade pitch controller is to regulate rotor speed while minimizing structural loads and pitch actuation. This controller is typically feedback only, relying on a generator speed measurement, and sometimes strain gages and accelerometers. A preview measurement of the incoming wind speed (from a turbine‐mounted lidar, for example) allows the addition of feedforward control, which enables improved performance compared with feedback‐only control. The performance improvement depends both on the amount of preview time available in the wind speed measurement and the coherence between the wind measurement and the wind that is actually experienced by the turbine. We show how to design a collective‐pitch optimal controller that takes both of these factors into account. Simulation results show significant improvement compared with baseline controllers and are well correlated with linear model‐based results. Linear model‐based results show the benefit of preview measurements for various preview times and measurement coherences. Copyright © 2016 John Wiley & Sons, Ltd.     

Analysis of aerodynamic load on straight-bladed vertical axis wind turbine

This paper presents a wind tunnel experiment for the evaluation of energy performance and aerodynamic forces acting on a small straight-bladed vertical axis wind turbine (VAWT) depending on several values of tip speed ratio. In the present study, the wind turbine is a four-bladed VAWT. The test airfoil of blade is symmetry airfoil (NACA0021) with 32 pressure ports used for the pressure measurements on blade surface. Based on the pressure distributions which are acted on the surface of rotor blade measured during rotation by multiport pressure-scanner mounted on a hub, the power, tangential force, lift and drag coefficients which are obtained by pressure distribution are discussed as a function of azimuthally position. And then, the loads which are applied to the entire wind turbine are compared with the experiment data of pressure distribution. As a result, it is clarified that aerodynamic forces take maximum value when the blade is moving to upstream side, and become small and smooth at downstream side. The power and torque coefficients which are based on the pressure distribution are larger than that by torque meter.     

Calibration of a spinner anemometer for wind speed measurements

The power curve of a wind turbine can be measured, according to IEC 61400‐12‐2 with a nacelle‐mounted anemometer. Typically, a sonic anemometer or a cup anemometer and a wind vane are mounted on the back of the nacelle roof. Another option is to use a spinner anemometer. The measurement principle of the spinner anemometer is based on the flow distortion caused by the wind turbine spinner. The flow on the spinner surface is measured by means of three 1D sonic sensors mounted on the spinner and a conversion algorithm to convert the wind velocity components measured by the three sonic sensors to horizontal wind speed, yaw misalignment and flow inclination angle. The algorithm utilizes two calibration constants that are specific to the spinner shape, blade root design and to the mounting positions of the sonic sensors on the spinner. The present analysis describes methods to determine the calibration constant related to wind speed measurements. The first and preferred method is based on the definition of the calibration constant and uses wind speed measurements during the stopped condition of the wind turbine. Two alternative methods that did not require the turbine to be stopped were investigated: one used relatively high wind speed measurements during normal operation of the wind turbine, while the other one used a CFD simulation of the flow over the spinner. The method that entails stopping the turbine in good wind conditions showed the best results and is recommended. The evaluation of uncertainty was not included in the present analysis. Copyright © 2016 The Authors Wind Energy Published by John Wiley & Sons Ltd.     

Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models

Wind farm control (WFC) algorithms rely on an estimate of the ambient wind speed, wind direction, and turbulence intensity in the determination of the optimal control setpoints. However, the measurements available in a commercial wind farm do not always carry sufficient information to estimate these atmospheric quantities. In this paper, a novel measure (“observability”) is introduced that quantifies how well the ambient conditions can be estimated with the measurements at hand through a model inversion approach. The usefulness of this measure is shown through several case studies. While the turbine power signals and the inter‐turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for WFC to sufficiently cover observability for any ambient condition. Further, generally, more wake interaction leads to a higher observability. Also, the mathematical framework presented in this article supports the straightforward notion that turbine power measurements provide no additional information compared with local wind speed measurements, implying that power measurements are superfluous. Irregular farm layouts result in a higher observability due to the increase in unique wake interaction. The findings in this paper may be used in WFC to predict which ambient quantities can (theoretically) be estimated. The authors envision that this will assist in the estimation of the ambient conditions in WFC algorithms and can lead to an improvement in the performance of WFC algorithms over the complete envelope of wind farm operation.     

Coherent Doppler lidar for wind farm characterization

Coherent Doppler lidar measurements are of increasing interest for the wind energy industry. Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three‐dimensional scanning Doppler lidar may provide a new basis for wind farm site selection, design and optimization. In this paper, the authors discuss Doppler lidar measurements obtained for a wind energy development. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically terrain effects, spatial variation of winds, power density and the effect of shear at different layers within the rotor swept area. Vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain‐following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. Doppler lidar data are used to estimate the spatial power density at hub height (for the period of the deployment). An example wind farm layout is presented for demonstration purposes based purely on lidar measurement, even though the lidar data acquisition period cannot be considered climatological. The strength of this approach is the ability to directly measure spatial variations of the wind field over the wind farm. Also, because Doppler lidar can measure winds at different vertical levels, an approach for estimating wind power density over the rotor swept area (rather than only the hub height) is explored. Finally, advanced vector retrieval algorithms have been applied to better characterize local wind variations and shear. Copyright © 2012 John Wiley & Sons, Ltd.     

Environmental contours for circular‐linear variables based on the direct sampling method

Environmental contours are often used in the design of engineering structures to identify extreme environmental conditions that may give rise to extreme loads and responses. The perhaps most common application of environmental contours is for wave climate variables such as significant wave height and wave period. However, for the design of wind energy installations, the joint distribution of wind speed and wind direction may be equally important. In this case, joint modelling of linear (wind speed) and circular (wind direction) variables are needed, and methods for establishing environmental contours for circular‐linear variables will be required. In this paper, different ways of establishing environmental contours for circular‐linear variables will be presented and applied to a joint distribution model for wind speed and wind direction. In particular, the direct sampling approach to environmental contours will be modified to the case where one of the variables is cyclic. In addition, contours based on exceedance planes in polar coordinates will be established, and circular‐linear contours will also be calculated based on the inverse FORM (I‐FORM) approach.     

Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.