A dynamical downscaling of ERA‐Interim in the North Sea using WRF with a 3 km grid—for wind resource applications

Large offshore wind energy projects are being planned and installed in the North Sea, and there is an urgent demand for high‐resolution atmospheric statistics to assess potential power production and revenue. Meteorological observations are too sparse to obtain those statistics, and global reanalyses like ERA‐Interim have a resolution too coarse in space and time to capture important small‐scale and terrain‐driven features of the atmospheric flow. We therefore dynamically downscale ERA‐Interim with the mesoscale model Weather Research and Forecasting to a 3 km grid to capture those unresolved features, for the period 1999–2008. The large‐scale flow is conditioned by spectral nudging, and we make use of observation nudging towards QuikSCAT near‐surface winds. The downscaling results in 100 m wind‐speed distributions and mean wind speeds, which are closer to the observations than ERA‐Interim, while the accuracy in terms of root‐mean‐square error decreases. The observation nudging partially counteracts this latter effect, improving the root‐mean‐square error of wind speed and direction by 0.5 m s^?1 and ~10°, respectively. We also introduce the power skill score, specifically designed to evaluate model performance within wind resource mapping. The power skill score confirms that the dynamical downscaling improves the distribution of wind speed in ranges where high accuracy is important for wind resource assessment. Copyright © 2016 John Wiley & Sons, Ltd.     

De‐trending of wind speed variance based on first‐order and second‐order statistical moments only

The lack of efficient methods for de‐trending of wind speed resource data may lead to erroneous wind turbine fatigue and ultimate load predictions. The present paper presents two models, which quantify the effect of an assumed linear trend on wind speed standard deviations as based on available statistical data only. The first model is a pure time series analysis approach, which quantifies the effect of non‐stationary characteristics of ensemble mean wind speeds on the estimated wind speed standard deviations as based on mean wind speed statistics only. This model is applicable to statistics of arbitrary types of time series. The second model uses the full set of information and includes thus additionally observed wind speed standard deviations to estimate the effect of ensemble mean non‐stationarities on wind speed standard deviations. This model takes advantage of a simple physical relationship between first‐order and second‐order statistical moments of wind speeds in the atmospheric boundary layer and is therefore dedicated to wind speed time series but is not applicable to time series in general. The capabilities of the proposed models are discussed by comparing model predictions with conventionally de‐trended characteristics of measured wind speeds using data where high sampled time series are available, and a traditional de‐trending procedure therefore can be applied. This analysis shows that the second model performs significantly better than the first model, and thus in turn that the model constraint, introduced by the physical link between the first and second statistical moments, proves very efficient in the present context. © 2013 The Authors. Wind Energy Published by John Wiley & Sons Ltd.     

A joint probability density function of wind speed and direction for wind energy analysis

A very flexible joint probability density function of wind speed and direction is presented in this paper for use in wind energy analysis. A method that enables angular–linear distributions to be obtained with specified marginal distributions has been used for this purpose. For the marginal distribution of wind speed we use a singly truncated from below Normal–Weibull mixture distribution. The marginal distribution of wind direction comprises a finite mixture of von Mises distributions. The proposed model is applied in this paper to wind direction and wind speed hourly data recorded at several weather stations located in the Canary Islands (Spain). The suitability of the distributions is judged from the coefficient of determination R².

The conclusions reached are that the joint distribution proposed in this paper: (a) can represent unimodal, bimodal and bitangential wind speed frequency distributions, (b) takes into account the frequency of null winds, (c) represents the wind direction regimes in zones with several modes or prevailing wind directions, (d) takes into account the correlation between wind speeds and its directions. It can therefore be used in several tasks involved in the evaluation process of the wind resources available at a potential site. We also conclude that, in the case of the Canary Islands, the proposed model provides better fits in all the cases analysed than those obtained with the models used in the specialised literature on wind energy.     

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.     

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.     

Wind energy estimation at Quetta

Hourly wind data at Quetta airport (Samungli) for the years 1984–1985, were obtained using a standard anemometer height of 10 m, with a view to work out the feasibility of wind energy utilization. Quetta (lat. 30°11 'N long. 66°57'E) is elevated at 1799 m above sea level. In this paper, we analyse the wind energy data by using the Weibull distribution. Scaling and shaping parameters are determined by using least-squares approximation to a straight line. Actual wind data, weighted Weibull density function and weighted Rayleigh probability density function for regular and continuous periods of 4 weeks up to a year (wind characteristics are being recorded and summarized as diurnal and monthly wind velocity distributions and wind power density roses) are plotted which shows that the Weibull distribution is generally of the right shape to fit low-averaged wind speed frequency curves. However, density function of a normal distribution is also determined. Deviations in wind speed distributions at very low-averaged wind speeds and at comparatively large-averaged wind speeds are found.     

An M‐Rice wind speed frequency distribution

Several known statistical distributions can describe wind speed data, the most commonly used being the Weibull family. In this paper, a new law, called ‘M‐Rice’, is proposed for modeling wind speed frequency distributions. 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 AutoRegressive Moving Average (ARMA) model where the noise term is ‘multifractal’, i.e. associated with a random cascade. This leads to the distribution of wind speeds according to the M‐Rice probability distribution function, i.e. a Rice distribution multiplicatively convolved with a normal law. A comparison based on the estimation of the mean wind speed and power density values as well as on the different goodness‐of‐fit tests (the Kolmogorov–Smirnov test, the Kuiper test and the quantile–quantile plot) was made between this new distribution and the Weibull distribution for 35 data sets of wind speed from the Netherlands and Corsica (France) sites. Accordingly, the M‐Rice and Weibull distributions provided comparable performances; however, the quantile–quantile plots suggest that the M‐Rice distribution provides a better fit of extreme wind speed data. Beyond these good results, our approach allows one to interpret the observed values of Weibull parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Short‐term spatio‐temporal prediction of wind speed and direction

This paper aims to produce a low‐complexity predictor for the hourly mean wind speed and direction from 1 to 6 h ahead at multiple sites distributed around the UK. The wind speed and direction are modelled via the magnitude and phase of a complex‐valued time series. A multichannel adaptive filter is set to predict this signal on the basis of its past values and the spatio‐temporal correlation between wind signals measured at numerous geographical locations. The filter coefficients are determined by minimizing the mean square prediction error. To account for the time‐varying nature of the wind data and the underlying system, we propose a cyclo‐stationary Wiener solution, which is shown to produce an accurate predictor. An iterative solution, which provides lower computational complexity, increased robustness towards ill‐conditioning of the data covariance matrices and the ability to track time‐variations in the underlying system, is also presented. The approaches are tested on wind speed and direction data measured at various sites across the UK. Results show that the proposed techniques are able to predict wind speed as accurately as state‐of‐the‐art wind speed forecasting benchmarks while simultaneously providing valuable directional information. Copyright © 2013 John Wiley & Sons, Ltd.     

Enhanced estimation of boundary layer advective properties to improve space‐to‐time conversion processes for wind energy applications

Remote sensing instruments that scan have the ability to provide high‐resolution spatial measurements of atmospheric boundary layer winds across a region. However, the ability to use these spatially distributed measurements to extract temporal variations in the flow at time scales less than the measurement revisit period is historically limited. As part of this work, the framework for an enhanced space‐to‐time conversion technique is established, allowing for time histories of atmospheric boundary layer wind characteristics to be reliably extracted for locations within the measurement domain. This space‐to‐time conversion technique is made possible by quantifying momentum advection within the measurement domain, rather than simply assuming a uniform advection based on a singular mean wind speed and direction. The use of this technique enables the extraction of long lead‐time (ie, upwards of 60 seconds) forecasts of wind speed and direction at individual locations within the measurement domain, thereby expanding the application and potential benefits of scanning instruments. For example, these long lead‐time forecasts can be used to enhance proactive wind turbine control and more accurately define wind turbine wake statistics.     

A novel empirical model for vertical profiles of downburst horizontal wind speed

This study proposes an empirical model for preliminary wind-resist design of downburst flow. Existing empirical models were compared with field data and found to underpredict horizontal wind speed below the height corresponding to the maximum radial velocity, due to the neglect of viscous effects and the evolution of vertical wind profiles along radial direction. To address these deficiencies, semi-empirical piecewise functions including wall shear effects in the local turbulent boundary layer and interpolation functions were proposed to improve the accuracy of existing models. The wind profile based on Coles' theory was found to agree well with field data, with the parabola interpolation function being the most desirable. Using the proposed method, the vertical profile of horizontal wind speed at different local radial locations can be predicted for wind resist design given the inlet wind speed of the downburst flow. Overall, this model improves upon existing empirical models and allows for more accurate wind-resist design.     

Determination of Weibull parameters for wind energy analysis of İzmir,Turkey

In this study, the two Weibull parameters of the wind speed distribution function, the shape parameter k (dimensionless) and the scale parameter c (ms^?1), were computed from the wind speed data for ?zmir. Wind data, consisting of hourly wind speed records over a 5‐year period, 1995–1999, were measured in the Solar/Wind‐Meteorological Station of the Solar Energy Institute at Ege University. Based on the experimental data, it was found that the numerical values of both Weibull parameters (k and c) for ?zmir vary over a wide range. The yearly values of k range from 1.378 to 1.634 with a mean value of 1.552, while those of c are in the range of 2.956–3.444 with a mean value of 3.222. The average seasonal Weibull distributions for ?zmir are also given. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution, with a special case of the Weibull distribution for k=2. As a result, the Weibull distribution is found to be suitable to represent the actual probability of wind speed data for ?zmir (at annual average wind speeds up to 3 ms^?1). Copyright © 2002 John Wiley & Sons, Ltd.     

Wind turbine extreme gust control

This paper focuses on the problem of extreme wind gust and direction change recognition (EG&DR) and control (EEC). An extreme wind gust with direction change can lead to large loads on the turbine (causing fatigue) and unnecessary turbine shutdowns by the supervisory system caused by rotor overspeed. The proposed EG&DR algorithm is based on a non‐linear observer (extended Kalman filter) that estimates the oblique wind inflow angle and the blade effective wind speed signals, which are then used by a detection algorithm (cumulative sum test) to recognize extreme events. The non‐linear observer requires that blade root bending moments measurements (in‐plane and out‐of‐plane) are available. Once an extreme event is detected, an EEC algorithm is activated that: (i) tries to prevent the rotor speed from exceeding the overspeed limit by fast collective blade pitching; and (ii) reduces 1p blade loads by means of individual pitch control algorithm, designed in an ?_∞ optimal control setting. The method is demonstrated on a complex non‐linear test turbine model. Copyright © 2009 John Wiley & Sons, Ltd.     

Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite

The QuikSCAT mission provided valuable daily information on global ocean wind speed and direction from July 1999 until November 2009 for various applications including numerical weather prediction, ocean and atmospheric modelling. One new and important application for wind vector satellite data is offshore wind energy, where accurate and frequent measurements are required for siting and operating modern wind farms. The greatest advantage of satellite observations rests in their extended spatial coverage. This paper presents analyses of the 10 year data set from QuikSCAT, for the overview of the wind characteristics observed in the North and Baltic Seas, where most of Europe's offshore wind farms operate and more will be constructed. Significant issues in data availability are identified, directly related to the flagging schemes. In situ observations from three locations in the North Sea are used for comparisons. Mean biases (in situ minus satellite) are close to zero for wind speed and ‐2.7° for wind direction with a standard deviation of 1.2 m s ^{? 1} and 15°, respectively. The impact of using QuikSCAT and in situ measurements extrapolated to 10 m for wind power density estimations is assessed, accounting for possible influences of rain‐contaminated retrievals, the sample size, the atmospheric stability effects and either fitting the Weibull distribution or obtaining the estimates from the time series of wind speed observations.Copyright © 2012 John Wiley & Sons, Ltd.     

Met‐ocean conditions influence on floating offshore wind farms power production

Met‐ocean conditions may affect the performance of a floating wind turbine, since a harsh climate could lead the system to exceed its operating thresholds and thus to force the machine shutdown. In this paper, it is a proposed methodology to evaluate the effect of met‐ocean conditions on the long‐term dynamic behaviour, and energy production, of a floating wind farm. For a sample of 500 MW farm located off the coast of Aberdeen (Scotland), 20 years of met‐ocean data are generated by means of meteorological reanalysis techniques. A subset of 1000 hourly conditions is selected, by means of a maximum dissimilarity algorithm, and input to a dynamic floating wind turbine model. Numerical results are then interpolated for the whole set of met‐ocean data, using radial basis functions. This approach allows to dramatically reduce the global computation time. Tower inclination and hub acceleration are chosen as relevant operating parameters: the former mainly depends on mean wind speed and direction, being largest at rated wind speed. The latter is also affected by significant wave height, and reaches its highest values when wind and waves are aligned. For each simulation, any machine exceeding the selected safety threshold is considered to be shut down. Assuming continuous operation, the average lifespan capacity factor of the farm is 50.2%; more restrictive tolerances result in a non‐linear reduction of the energy production. This approach may help both at the design and the operational stage, in determining the best trade‐off between energy production and safe operation. Copyright © 2015 John Wiley & Sons, Ltd.     

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R² and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.     

Evaluation of a wind speed estimator for effective hub‐height and shear components

Estimates of the effective wind speed disturbances acting on a wind turbine are useful in a variety of control applications. With some simplifications, it is shown that for zero yaw error, any wind field interacting with a turbine can be equivalently described using a hub‐height (uniform) component as well as linear horizontal and vertical shear components. A Kalman filter‐based wind speed estimator is presented for estimation of these effective hub‐height and shear components. The wind speed estimator is evaluated in the frequency domain using the FAST aeroelastic simulator with the National Renewable Energy Laboratory's 5 MW reference wind turbine model and realistic hub‐height and shear disturbances. In addition, the impact of the inflow model, used to simulate the rotor aerodynamics, on the Kalman filter performance is investigated. It is found that the estimator accuracy strongly depends on the inflow model used. In general, the estimator performs well up to a bandwidth of 1 Hz when the inflow model used for simulation matches the model used to create the linear Kalman filter model and blade pitch angle remains close to the linearization operating point. However, inaccuracies in the linear model of the turbine when dynamic inflow is used for simulation as well as nonlinearities in the turbine dynamics due to blade pitch actuation cause performance to degrade. Finally, the improvement gained by employing a non‐causal wind speed estimator is assessed, showing a minor increase in performance. Copyright © 2014 John Wiley & Sons, Ltd.     

A spinner‐integrated wind lidar for enhanced wind turbine control

A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar‐measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed‐forward enabled turbine controllers is discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

基于BP神经网络方法的风电场风速插补分析应用

[目的]准确的风资源数据对风场的风资源评估和发电量计算有着重大意义.由于机械故障、天气因素和人为影响等原因,风场内风速数据出现采集时间短、间断点多、数据失真等诸多问题,给风资源的评估带来不小的麻烦.[方法]现阶段风电行业内采用基于相关测量预测方法(MCP,Measure-Correlate Predict)(可称之为传...     

Fitting wind speed distributions: A case study

The wind speed data represented in the form of frequency curves show the shape of a potential model. The Weibull and Lognormal models are used for this purpose, with hourly mean wind speed data. This study deals with the estimation of the annual Weibull and Lognormal parameters from 20 locations in Navarre. The suitability of both distributions is judged from the R² coefficient with a linear regression for the Weibull distribution and a nonlinear regression for the Lognormal distribution. Both approaches give a good fit, giving better results for the Weibull distribution. A comparison between the estimation and the production for a wind farm is offered.     

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

Following its commitment to Paris Agreement in 2015, China has started to explore potential renewable energy solutions with low carbon emissions to mitigate global warming. Though wind energy is one of the most cost‐effective solutions and has been favored for climate policy development around the world, its high sensitivity to climate change raises some critical issues for the long‐term effectiveness in providing sustainable energy supply. Particularly, how wind speed and its energy potential in China will change in the context of global warming is still not well understood. In this paper, we simulate the near‐surface wind speed over China using the PRECIS regional climate modeling system under different RCP emission scenarios for assessing the possible changes in wind speed and wind energy availability over China throughout the 21^st century. Overall, the PRECIS model can reasonably reproduce the mesoscale climatological near‐surface wind speed and directions as documented in reanalysis data across most regions of China, while some local discrepancies are reported in the southwestern regions. In the future, the annual mean wind speed would be decreasing in most regions of China, except for a slightly increase in the southeast. The expected changes in wind speed are characterized with different amplitudes and rates under different RCP emission scenarios. The changes in the spatial distribution of wind speed seem to be sensitive for RCP climate emission scenarios, especially in the late 21^st century. The spatiotemporal changes in wind energy potential exhibit a similar behavior to those in near‐surface wind speed, but the magnitudes of these changes are larger. In general, the wind power density is expected to increase by over 5% in winter in the major wind fields in China (ie, Northwest, Northcentral and Northeast), while significant decreases (by about 6% on average) are projected for other seasons (ie, spring, summer and autumn). By contrast, the wind energy potential in the northeast would increase over most months in the year, especially in winter and summer. The results of this research are of great importance for understanding where and to what extent the wind energy can be utilized to contribute renewable energy system development in China in support of its long‐term climate change mitigation commitment.