Real-time three-dimensional wind simulation for windmill rig tests

This paper deals with the methods of three-dimensional fixed-point wind speed real-time simulation modelled in large band, in order to use them in test rigs for experimental investigation of the wind energy conversion systems. The medium- and long-term components of the non-stationary wind speed are considered as known, being issued from measured data or by adopting a generic model. The spectral characteristics of three-dimensional turbulence are described either by the Kaimal or the von Karman models. The turbulence intensity and the length scale that take part in these models are calculated by the site parameters, using current standards. The basic idea of the methods for large-band three-dimensional wind simulation is to use rational shaping filters that approximate non-integer orders shaping filters issued from the Kaimal and the von Karman models. All the synthesized rational shaping filters use one time constant, automatically adapted to the medium- and long-term components that pilot the other time constants of the shaping filters by a set of parameters practically constant. Some numerical results concerning time series that simulate the non-stationary wind speed with three-dimensional turbulence components based on Kaimal and von Karman models are presented.     

Correlation of wind speed between neighboring measuring stations

A method for establishing wind speed correlation between neighboring measuring stations is presented in this paper. The aim of this study is to develop a model, in which given the wind speed at a particular site to simulate the wind speed at another, nearby site, in order to estimate the wind power of an area. This method takes into account the evolution of the sample cross correlation function (SCCF) of wind speed in time domain and uses an artificial neural network to perform the wind speed simulation. Four separate pairs of wind data measuring stations at two different regions were examined. Tests showed that the higher the SCCF value between two sites, the better simulation achieved. Also, in a pair of stations under investigation the reference station must be the one that contains more information in its wind speed signal, in order to obtain the optimum simulation performance.     

Modelling of turbulent wind flow using the embedded Markov chain method

Small wind turbines are usually installed to provide off-grid power and as such can be situated close to the load in a less-than-ideal wind resource. These wind regimes are often governed by low mean speeds and high wind turbulence. This can result in energy production less than that specified by the manufacturer's power curve. Wind turbulence is detrimental to the fatigue life of key components and overall turbine reliability and therefore must be considered in the design stage of small wind turbines. Consequently it is important to accurately simulate wind speed data at highly turbulent sites to quantify loading on turbine components. Here we simulate wind speed data using the Markov chain Monte Carlo process and incorporate long term effects using an embedded Markov chain. First, second and third order Markov chain predictions were found to be in good agreement with measured wind data acquired at 1 Hz. The embedded Markov chain was able to predict site turbulent intensity with a reasonable degree of accuracy. The site exhibited distinctive peaks in wind speed possibly caused by diurnal heating and cooling of the earth's surface. The embedded Markov chain method was able to simulate these peaks albeit with a time offset.     

Simulation of hourly wind speed and array wind power

Statistical summaries of wind speed are sufficient to compute many characteristics of turbine-generated power, such as the mean, variance and reliability of various power levels. However, a wind speed time series is necessary to produce a sequence of power values as used for investigating load matching and storage requirements. Since a long historical record of wind speed may not be available at a wind turbine candidate site, it is desirable to be able to generate a simulated numerical sequence of hourly wind speed values. Two such approximate procedures are developed in this paper. One procedure generates sequential wind speed values at a site based on the Weibull parameters of hourly wind speed and the lag-one autocorrelation of hourly wind speed values. Comparison with historical data at a site is made. The second procedure generates sequential hourly wind power values for a regional array of wind turbines. It utilizes the typical site wind characteristics, the spatial and lag-one cross correlation and autocorrelation of hourly wind speed values and an equivalent linearized relationship between array average wind speed and array power. Comparison with results for six different wind turbines in three different regional arrays indicates good agreement for wind power histograms, autocorrelation function and mean persistence.     

Assessment of turbulence intensity of local wind regimes

This paper analyses the turbulence intensity of seven different sites in the coastal region of South Africa. The study is based on 12 months, 10-minute average wind speed measurement. The turbulence intensity is calculated using mean wind speed and the standard deviation method. Some of the key results show that generalised extreme value distribution gives the best fit to the turbulence intensity in the entire sites. The annual shape parameter of the distribution ranges from 0.11 at site WM06 to 0.3 at site WM05. The scale parameter varies from 3.55 at site WM04 to 6.06 at site WM02, while the location parameters span between 7.07 at site WM04 and 10.65 at site WM01. The annual turbulence intensity varies from 9.6% at site WM04 to 15.9% at site WM03. The result also reveals that sites with higher turbulence intensity have lower wind power potential and hence are less attractive for wind power application.     

Spectral tensor parameters for wind turbine load modeling from forested and agricultural landscapes

A velocity spectral tensor model was evaluated from the single‐point measurements of wind speed. The model contains three parameters representing the dissipation rate of specific turbulent kinetic energy, a turbulence length scale and the turbulence anisotropy. Sonic anemometer measurements taken over a forested and an agricultural landscape were used to calculate the model parameters for neutral, slightly stable and slightly unstable atmospheric conditions for a selected wind speed interval. The dissipation rate above the forest was nine times that at the agricultural site. No significant differences were observed in the turbulence length scales between the forested and agricultural areas. Only a small difference was observed in the turbulence anisotropy at the two sites, except near the surface, where the forest turbulence was more isotropic. The turbulence anisotropy remained more or less constant with height at the forest site, whereas the turbulence became more isotropic with height for the agricultural site. Using the three parameters as inputs, we quantified the performance of the model in coherence predictions for vertical separations. The model coherence of all the three velocity components was overestimated for the analyzed stability classes at both sites. As expected from the model approximations, the model performed better at both sites for neutral stability than slightly stable and unstable conditions. The model prediction of coherence of the along‐wind and vertical components was better than that of the cross‐wind component. No significant difference was found between the performance of the model at the forested and the agricultural areas. © 2014 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Observations of wind turbine wakes and surface roughness effects on wind flow variability

Wind data collected at nine meteorological towers at the Goodnoe Hills MOD-2 wind turbine site were analyzed to characterize the wind flow over the site both in the absence and presence of wind turbine wakes. Free-flow characteristics examined were the variability of wind speed and turbulence intensity across the site as a function of wind direction and surface roughness. The nine towers' data revealed that scattered areas of trees upwind of the site caused pronounced variations in the wind flow over the site. At two towers that were frequently downwind of an extensive grove of trees, up to 30% reductions in wind speed and a factor of 2 to 3 increase in turbulence intensity were measured. A substantial increase in the magnitude of the wind gusts, as well as a considerable decrease in the mean wind speed, was observed when a tower was downwind of the trees.Wind turbine wake characteristics analyzed included the average velocity deficits, wake turbulence, wake width, wake trajectory, vertical profile of the wake, and the stratification of wake properties as a function of the ambient wind speed and turbulence intensity. The wind turbine rotor disk spanned a height of 15 m to 107 m. The nine towers' data permitted a detailed analysis of the wake behavior at a height of 32 m at various downwind distances from 2 to 10 rotor diameters (D). The relationship between velocity deficit and downwind distance was surprisingly linear, with average maximum deficits ranging from 34% at 2 D to 7% at 10 D. Largest deficits were at low wind speeds and low turbulence intensities. Average wake widths were 2.8 D at a downwind distance of 10 D. Implications for turbine spacing are that, for a wind farm with a 10-D row separation, array losses would be significantly greater for a 2-D than a 3-D spacing because of incremental effects caused by overlapping wakes. Other interesting wake properties observed were the wake turbulence (which was greatest along the flanks of the wake). the vertical variation of deficits (which were greater below hub height than above), and the trajectory of the wake (which was essentially straight).     

Application of a LES technique to characterize the wake deflection of a wind turbine in yaw

When a wind turbine works in yaw, the wake intensity and the power production of the turbine become slightly smaller and a deflection of the wake is induced. Therefore, a good understanding of this effect would allow an active control of the yaw angle of upstream turbines to steer the wake away from downstream machines, reducing its effect on them. In wind farms where interaction between turbines is significant, it is of interest to maximize the power output from the wind farm as a whole and to reduce fatigue loads on downstream turbines due to the increase of turbulence intensity in wakes. A large eddy simulation model with particular wind boundary conditions has been used recently to simulate and characterize the turbulence generated by the presence of a wind turbine and its evolution downstream the machine. The simplified turbine is placed within an environment in which relevant flow properties like wind speed profile, turbulence intensity and the anisotropy of turbulence are found to be similar to the ones of the neutral atmosphere. In this work, the model is used to characterize the wake deflection for a range of yaw angles and thrust coefficients of the turbine. The results are compared with experimental data obtained by other authors with a particle image velocimetry technique from wind tunnel experiments. Also, a comparison with simple analytical correlations is carried out. Copyright © 2009 John Wiley & Sons, Ltd.     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

This paper proposes the use of a new Measure-Correlate-Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R²) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.     

Study of the turbulent characteristics of the near-wake field of a medium-sized wind turbine operating in high wind conditions

The near-wake turbulent structure that is downwind of a medium-sized, horizontal axis wind turbine at a distance of one rotor diameter is discussed. The experimental site is the Samos Island Wind Park comprising nine wind turbines installed on the top of a 400 m-high saddle. The analysis is based on experimental data obtained mainly under strong wind conditions by two masts erected upstream and downstream of a wind turbine. The field of wind turbulence is examined both in integral and spectral form. Consideration of the perturbation produced by the tower construction is crucial in the interpretation of results. Observations show that the turbulent field varies from the edge to the center of the wake and strongly depends on the incident wind speed. Increased turbulent levels are observed near the blade tips, with evidence of a similar trend around the hub height for all wind speeds. Decreases of wind turbulence are observed in mid frequencies inside the wake due to the reduced shear associated with the flat crosswind velocity profile. This effect seems to dominate in the variation of the integral values of the longitudinal wind component variance. The low frequency portion of wind spectra reverses behavior in high wind speeds, i.e., an increase in energy relative to background values is observed. This is probably due to the shape of the turbine characteristic power curve. Cross-wind profiles of turbulent shear stresses at the lower boundary of the wake are also discussed.     

Influence of atmospheric stability on wind turbine loads

Simulations of wind turbine loads for the NREL 5 MW reference wind turbine under diabatic conditions are performed. The diabatic conditions are incorporated in the input wind field in the form of wind profile and turbulence. The simulations are carried out for mean wind speeds between 3 and 16 m s ^{? 1} at the turbine hub height. The loads are quantified as the cumulative sum of the damage equivalent load for different wind speeds that are weighted according to the wind speed and stability distribution. Four sites with a different wind speed and stability distribution are used for comparison. The turbulence and wind profile from only one site is used in the load calculations, which are then weighted according to wind speed and stability distributions at different sites. It is observed that atmospheric stability influences the tower and rotor loads. The difference in the calculated tower loads using diabatic wind conditions and those obtained assuming neutral conditions only is up to 17%, whereas the difference for the rotor loads is up to 13%. The blade loads are hardly influenced by atmospheric stability, where the difference between the calculated loads using diabatic and neutral input wind conditions is up to 3% only. The wind profiles and turbulence under diabatic conditions have contrasting influences on the loads; for example, under stable conditions, loads induced by the wind profile are larger because of increased wind shear, whereas those induced by turbulence are lower because of less turbulent energy. The tower base loads are mainly influenced by diabatic turbulence, whereas the rotor loads are influenced by diabatic wind profiles. The blade loads are influenced by both, diabatic wind profile and turbulence, that leads to nullifying the contrasting influences on the loads. The importance of using a detailed boundary‐layer wind profile model is also demonstrated. The difference in the calculated blade and rotor loads is up to 6% and 8%, respectively, when only the surface‐layer wind profile model is used in comparison with those obtained using a boundary‐layer wind profile model. Finally, a comparison of the calculated loads obtained using site‐specific and International Electrotechnical Commission (IEC) wind conditions is carried out. It is observed that the IEC loads are up to 96% larger than those obtained using site‐specific wind conditions.Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of light detection and ranging wind speed measurements for wind turbine control

Light detection and ranging (LIDAR) systems are able to measure the speed of incoming wind before it reaches a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine structural loads. However, the degree to which such preview‐based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. This study examines the accuracy of different measurement scenarios that rely on coherent continuous‐wave or pulsed Doppler LIDAR systems, in terms of root‐mean‐square measurement error, to determine their applicability to feedforward control. In particular, the impacts of measurement range, angular offset of the LIDAR beam from the wind direction, and measurement noise are studied for various wind conditions. A realistic simulation case involving a scanning LIDAR unit mounted in the spinner of a MW‐scale wind turbine is studied in depth, with emphasis on preview distances that provide minimum measurement error for a specific scan radius. Measurement error is analyzed for LIDAR‐based estimates of point wind speeds at the rotor as well as spanwise averaged blade effective wind speeds. The impact of turbulence structures with high coherent turbulent kinetic energy on measurement error is discussed as well. Copyright © 2013 John Wiley & Sons, Ltd.     

Flicker study on variable speed wind turbines with doubly fed induction generators

Grid connected wind turbines may produce flicker during continuous operation. This paper presents a simulation model of a MW-level variable speed wind turbine with a doubly fed induction generator developed in the simulation tool of PSCAD/EMTDC. Flicker emission of variable speed wind turbines with doubly fed induction generators is investigated during continuous operation, and the dependence of flicker emission on mean wind speed, wind turbulence intensity, short circuit capacity of grid and grid impedance angle are analyzed. A comparison is done with the fixed speed wind turbine, which leads to a conclusion that the factors mentioned above have different influences on flicker emission compared with that in the case of the fixed speed wind turbine. Flicker mitigation is realized by output reactive power control of the variable speed wind turbine with doubly fed induction generator. Simulation results show the wind turbine output reactive power control provides an effective means for flicker mitigation regardless of mean wind speed, turbulence intensity and short circuit capacity ratio.     

Periodic pulsations from a three-bladed wind turbine

In this paper, periodic power pulsations from a three-bladed wind turbine are analyzed. The influence of wind shear, wind speed, turbulence intensity, rotor position and tower oscillation is investigated. No clear dependence between the periodic power components and the wind shear or turbulence intensity has been verified. The investigated turbine sometimes produces large power pulsations at the tower resonance frequency. These occur when the turbine oscillates in the sideways direction of the nacelle     

Evaluation of different wind fields for the investigation of the dynamic response of offshore wind turbines

As the size of offshore wind turbines increases, a realistic representation of the spatiotemporal distribution of the incident wind field becomes crucial for modeling the dynamic response of the turbine. The International Electrotechnical Commission (IEC) standard for wind turbine design recommends two turbulence models for simulations of the incident wind field, the Mann spectral tensor model, and the Kaimal spectral and exponential coherence model. In particular, for floating wind turbines, these standard models are challenged by more sophisticated ones. The characteristics of the wind field depend on the stability conditions of the atmosphere, which neither of the standard turbulence models account for. The spatial and temporal distribution of the turbulence, represented by coherence, is not modeled consistently by the two standard models. In this study, the Mann spectral tensor model and the Kaimal spectral and exponential coherence model are compared with wind fields constructed from offshore measurements and obtained from large‐eddy simulations. Cross sections and durations relevant for offshore wind turbine design are considered. Coherent structures from the different simulators are studied across various stability conditions and wind speeds through coherence and proper orthogonal decomposition mode plots. As expected, the standard models represent neutral stratification better than they do stable and unstable. Depending upon the method used for generating the wind field, significant differences in the spatial and temporal distribution of coherence are found. Consequently, the computed structural design loads on a wind turbine are expected to vary significantly depending upon the employed turbulence model. The knowledge gained in this study will be used in future studies to quantify the effect of various turbulence models on the dynamic response of large offshore wind turbines.     

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator (DFIG) wind turbines. At present, to the best of the authors’ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabilistic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table (COPT), considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.     

Regulation of WTG dynamic response to parameter variations of analytic wind stochasticity

Although variable‐speed operation can reduce the impact of transient wind gusts and subsequent component fatigue, this is still an unknown factor that must now be quantified. Reduction in drive‐train stresses caused by fatigue loads in high wind turbulence is fundamental to realizing both output power leveling and long service life for a wind turbine generator (WTG). This paper presents an evolutionary controller comprising a linear quadratic Gaussian (LQG) and neurocontroller acting in tandem to effect optimal performance under high turbulence intensities, for a variable‐speed, fixed‐pitch WTG. The control objectives are maximum energy conversion and reduction in mechanical stresses on the system components. The proposed paradigm utilizes generator torque in controlling the rotor speed in relation to the highly turbulent wind speed, thereby ensuring the extracted aerodynamic power is maintained at a constant value, while shaft moments are regulated. The performance of the proposed controller is compared with that of the LQG and it is found that the former is more efficient in maintaining rated power, minimizing shaft torque variations, and shows robustness to parameter variations. Copyright © 2007 John Wiley & Sons, Ltd.     

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind‐turbine wakes for different hub‐height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met‐tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub‐height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near‐wake velocity field, while it affects noticeably the far‐wake evolution and recovery. A secondary effect on wake‐recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake‐generated turbulence fosters wake recovery. A semi‐empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models.     

Parabolic RANS solver for low‐computational‐cost simulations of wind turbine wakes

A numerical framework for simulations of wake interactions associated with a wind turbine column is presented. A Reynolds‐averaged Navier‐Stokes (RANS) solver is developed for axisymmetric wake flows using parabolic and boundary‐layer approximations to reduce computational cost while capturing the essential wake physics. Turbulence effects on downstream evolution of the time‐averaged wake velocity field are taken into account through Boussinesq hypothesis and a mixing length model, which is only a function of the streamwise location. The calibration of the turbulence closure model is performed through wake turbulence statistics obtained from large‐eddy simulations of wind turbine wakes. This strategy ensures capturing the proper wake mixing level for a given incoming turbulence and turbine operating condition and, thus, accurately estimating the wake velocity field. The power capture from turbines is mimicked as a forcing in the RANS equations through the actuator disk model with rotation. The RANS simulations of the wake velocity field associated with an isolated 5‐MW NREL wind turbine operating with different tip speed ratios and turbulence intensity of the incoming wind agree well with the analogous velocity data obtained through high‐fidelity large‐eddy simulations. Furthermore, different cases of columns of wind turbines operating with different tip speed ratios and downstream spacing are also simulated with great accuracy. Therefore, the proposed RANS solver is a powerful tool for simulations of wind turbine wakes tailored for optimization problems, where a good trade‐off between accuracy and low‐computational cost is desirable.