高空风力发电发展现状及关键技术研究综述

高空风力发电系统（AWES）主要通过采用系留航空器在一定高度下捕获稳定的风能并将其转化为电能,具有低成本、高效率、无污染等优势,近年来受到较大关注。本文介绍了几种AWES技术的基本原理、发展历程以及应用现状,并对几种AWES技术的结构特点、发电成本等进行了分析和比较,以期为我国未来发展AWES技术提供参考。     

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.     

Real‐time wind field reconstruction from LiDAR measurements using a dynamic wind model and state estimation

The use of light detection and ranging (LiDAR) instruments offer many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio‐temporal velocity measurements of the wind field. Moreover, LiDAR measurements only provide the radial (line‐of‐sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so‐called ‘cyclops’ dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real‐time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an unscented Kalman filter that employs a low‐order dynamic model of the wind to estimate the unmeasured velocities within the wind field, using repeated measurement updates from typical nacelle‐mounted LiDAR instruments. This approach is validated upon synthetic data generated from large eddy simulations of the atmospheric boundary layer. The accuracy of the wind field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind field estimates for use in preview turbine pitch control systems. © 2014 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.     

One‐year observations of the wind distribution and low‐level jet occurrence at Braunschweig,North German Plain

A data set consisting of one‐year vertical profiles of horizontal wind speed obtained with lidar at Braunschweig Airport, North German Plain, is analyzed with respect to the low‐level jet (LLJ). The observations reveal a typical LLJ altitude between 80 and 360 m, a frequency of occurrence up to almost 9% for some altitudes, and a typical wind speed between 4 and 9 m s^?1. LLJ events occurred most frequently in summer during the night. In the winter, LLJs were observed both during day and night. The Weibull distribution for wind speed is presented for different heights. The most probable wind speed of the Weibull distribution increases from 4 m s^?1 at 40 m altitude to values exceeding 7 m s^?1 for altitudes above 240 m. There is a significant difference for the Weibull parameters determined with a monthly, seasonal and annual data set. The contribution of the LLJ to the overall wind speed distribution is analyzed. An LLJ event occurred on 52% of the days over the year, with a total measurement time of 739 h. As the typical rated speed for onshore wind turbines is in the range from 11.5 to 14.5 m s^?1 and the typical hub height is in the range of 100 to 150 m, it can be expected that wind turbines are affected by the LLJ. Copyright © 2015 John Wiley & Sons, Ltd.     

Upwind preview to a horizontal axis wind turbine: a wind tunnel and field‐scale study

Data collected at the Eolos wind research facility and in the Saint Anthony Falls Laboratory atmospheric boundary layer wind tunnel are used to study the impact of turbulent inflow conditions on the performance of a horizontal axis wind turbine on flat terrain. The Eolos test facility comprises a 2.5MW Clipper Liberty C96 wind turbine, a meteorological tower and a WindCube LiDAR wind profiler. A second set of experiments was completed using particle image velocimetry upwind and in a wake of a miniature turbine in the wind tunnel to complement LiDAR measurements near the Eolos turbine. Joint statistics, most notably temporal cross‐correlations between wind velocity at different heights and turbine performance, are presented and compared at both the laboratory and field scales. The work (i) confirms that the turbine exerts a blockage effect on the mean flow and (ii) suggests a key, specific elevation, above hub height, where the incoming velocity signal is statistically most relevant to turbine operation and control. Wind tunnel measurements confirm such indication and suggest that hub height velocity measurements are optimal for wind preview and/or as input for active control strategies in aligned turbine configurations. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of offshore extreme wind from wind‐wave coupled modeling

A coupledwind‐wave modeling system is used to simulate 23 years of storms and estimate offshore extreme wind statistics. In this system, the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) are coupled, through a wave boundary layer model (WBLM) that is implemented in SWAN. The WBLM calculates momentum and turbulence kinetic energy budgets, using them to transfer wave‐induced stress to the atmospheric modeling. While such coupling has a trivial impact on the wind modeling for 10‐m wind speeds less than 20 ms^?1, the effect becomes appreciable for stronger winds—both compared with uncoupled WRF modeling and with standard parameterization schemes for roughness length. The coupled modeling output is shown to be satisfactory compared with measurements, in terms of the distribution of surface‐drag coefficient with wind speed. The coupling is also shown to be important for estimation of extreme winds offshore, where the WBLM‐coupled results match observations better than results from noncoupled modeling, as supported by measurements from a number of stations.     

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.     

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.     

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

Accurate short‐term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high‐resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower‐resolution models. Recent computational advances have enabled the use of large‐eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence‐resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof‐of‐concept study on the prospect of leveraging these ultra high‐resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high‐frequency information is lost. Therefore, a statistical post‐processing approach is explored on the basis of smoothing and feature engineering from the high‐frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.     

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.     

Assessing atmospheric stability and its impacts on rotor‐disk wind characteristics at an onshore wind farm

As the average hub height and blade diameter of new wind turbine installations continue to increase, turbines typically encounter higher wind speeds, which enable them to extract large amounts of energy, but they also face challenges due to the complex nature of wind flow and turbulence in the planetary boundary layer (PBL). Wind speed and turbulence can vary greatly across a turbine's rotor disk; this variability is partially due to whether the PBL is stable, neutral or convective. To assess the influence of stability on these wind characteristics, we utilize a unique data set including observations from two meteorological towers, a surface flux tower and high‐resolution remote‐sensing sound detection and ranging (SODAR) instrument. We compare several approaches to defining atmospheric stability to the Obukhov length (L). Typical wind farm observations only allow for the calculation of a wind shear exponent (α) or horizontal turbulence intensity (I_U) from cup anemometers, whereas SODAR gives measurements at multiple heights in the rotor disk of turbulence intensity (I) in the latitudinal (I_u), longitudinal (I_v) and vertical (I_w) directions and turbulence kinetic energy (TKE). Two methods for calculating horizontal Ifrom SODAR data are discussed. SODAR stability parameters are in high agreement with the more physically robust L,with TKE exhibiting the best agreement, and show promise for accurate characterizations of stability. Vertical profiles of wind speed and turbulence, which likely affect turbine power performance, are highly correlated with stability regime. At this wind farm, disregarding stability leads to over‐assessments of the wind resource during convective conditions and under‐assessments during stable conditions. Copyright © 2011 John Wiley & Sons, Ltd.     

A statistical analysis of wind energy potential at the eastern region of Saudi Arabia

The energy potential of wind for the eastern region of Saudi Arabia is investigated based on measurements of a complete year data at a coastal location in eastern Saudi Arabia. A suitable Weibull distribution is generated and a comparison of this model is made with the Rayleigh distribution of wind power densities. Two horizontal‐axis type of wind energy conversion systems which operate at fixed rpm are considered for the determination of the extractable wind power, and a model of quadratic power output function is used between the cut‐in speed and rated speed. It is shown that small‐scale wind energy systems are suitable in the eastern part of Saudi Arabia for power generation and irrigation purposes. Copyright © 1999 John Wiley & Sons, Ltd.     

An onsite demonstration and validation of LiDAR technology for wind energy resource assessment

The investigation of wind resource at higher heights is very crucial in planning wind power project. Normally, this involves the installation of a high and costly meteorological mast with a cup anemometer and wind vanes. This investigation uses the new ground-based remote-sensing technique Light Detection and Ranging (LiDAR) to investigate the wind resource at higher heights. This paper describes the LiDAR technology principle and examines the potential of LiDAR measurement to estimate the wind resource at higher heights by conducting a measurement campaign at Tamil Nadu, India. The wind statistics were determined using the 10?min average time-series wind data monitored by ZephIR LiDAR. These include the Weibull parameters, daily mean wind speed, wind power density, wind energy density, vertical wind speed profile and capacity factor. The investigation reveals that the vertical wind speed profile measured from the LiDAR system has approximate closer values to the standard meteorological measurement.     

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

One‐way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in‐house high‐resolution large‐eddy simulation code (UTD‐WF). Each simulation contains five nested WRF domains, with the largest domain spanning the north Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD‐WF LES higher‐resolution domain for a subset of six turbines at a resolution of ～5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met‐tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD‐WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD‐WF high‐resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models.     

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.     

Sensitivity of southern California wind energy to turbine characteristics

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd.     

Downwind pre‐aligned rotors for extreme‐scale wind turbines

Downwind force angles are small for current turbines systems (1–5 MW) such that they may be readily accommodated by conventional upwind configurations. However, analysis indicates that extreme‐scale systems (10–20 MW) will have larger angles that may benefit from downwind‐aligned configurations. To examine potential rotor mass reduction, the pre‐alignment concept was investigated a two‐bladed configuration by keeping the structural and aerodynamic characteristics of each blade fixed (to avoids a complete blade re‐design). Simulations for a 13.2 MW rated rotor at steady‐state conditions show that this concept‐level two‐bladed design may yield 25% rotor mass savings while also reducing average blade stress over all wind speeds. These results employed a pre‐alignment on the basis of a wind speed of 1.25 times the rated wind speed. The downwind pre‐aligned concept may also reduce damage equivalent loads on the blades by 60% for steady rated wind conditions. Even higher mass and damage equivalent load savings (relative to conventional upwind designs) may be possible for larger systems (15–20 MW) for which load‐alignment angles become even larger. However, much more work is needed to determine whether this concept can be translated into a practical design that must meet a wide myriad of other criteria. Copyright © 2017 John Wiley & Sons, Ltd.     

Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy

The feasibility of predicting the long-term wind resource at 22 UK sites using a measure-correlate-predict (MCP) approach based on just three months onsite wind speed measurements has been investigated. Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site. The accuracy of the predicted parameters of mean wind speed, mean wind power density, standard deviation of wind speeds and the Weibull shape factor was assessed, and their associated error distributions were investigated, using long-term measurements recorded over a period of 10 years. For each site, 120 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations. Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements. Averaged across 22 sites and all seasons, the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed of 0.21 ms⁻¹ and 4.8% respectively, and in the mean wind power density of 11 Wm⁻² and 14%. The average errors were reduced to 3.6% in the mean wind speed and 10% in the mean wind power density when using the optimum season for onsite wind measurements. These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling. The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment toolkit for small-scale wind developers.     

The influence of the Weibull assumption in monthly wind energy estimation

An estimation of the monthly wind energy output for the period 1999–2003 at five wind farms in northeastern Spain was evaluated. The methodology involved the calculation of wind speed histograms and the observed average wind power versus wind relation obtained from hourly data. The energy estimation was based on the cumulated contribution of the wind power from each wind speed interval. The impact of the Weibull distribution assumption as a substitute of the actual histogram in the wind energy estimation was evaluated. Results reveal that the use of a Weibull probability distribution has a moderate impact in the energy calculation as the largest estimation errors are, on average, no larger than 10% of the total monthly energy produced. However, the evaluation of the goodness of fit through the χ² statistics shows that the Weibull assumption is not strictly substantiated for most of the sites. This apparent discrepancy is based on the partial cancellation of the positive and negative departures of the Weibull fitted and the actual wind frequency distributions. Further investigation of the relation between the χ² and the error contribution exposes a tendency of the Weibull distribution to underestimate (overestimate) the observed histograms in the lower and upper (intermediate) wind speed intervals. This fact, together with the larger wind power weight over the highest winds, results in a systematic total wind energy underestimation. Copyright © 2008 John Wiley & Sons, Ltd.