Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.     

A new turbulence model for offshore wind turbine standards

Correct turbulence intensity modeling is crucial for fatigue load estimation for wind turbine structural design. It is well known that the International Electrotechnical Commission 61400‐3 Normal Turbulence Model recommended for offshore wind turbine design is not representative of offshore wind conditions. A new model is urgently needed as offshore wind energy is rapidly developing worldwide. After evaluating the suitability of the Normal Turbulence Model at three sites in Asia, Europe and the USA, it is found that wind–wave interaction and stability correction should be taken into account in modeling the offshore turbulence intensity and wind speed relationship. Therefore, a new turbulence intensity model, which models wind–wave interaction with the Charnock equation and adjusts for the influence of atmospheric stability through empirical turbulence scaling functions for the unstable atmospheric boundary layer, was developed. The new model is physically based and is tested against observations from the three sites. It shows better performance than the Normal Turbulence Model and hence is recommended to replace the Normal Turbulence Model. For model application, only two parameters are required, which are defined herein to represent offshore sites with high, medium and low turbulence intensities. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm

A modeling framework is proposed and validated to simulate turbine wakes and associated power losses in wind farms. It combines the large-eddy simulation (LES) technique with blade element theory and a turbine-model-specific relationship between shaft torque and rotational speed. In the LES, the turbulent subgrid-scale stresses are parameterized with a tuning-free Lagrangian scale-dependent dynamic model. The turbine-induced forces and turbine-generated power are modeled using a recently developed actuator-disk model with rotation (ADM-R), which adopts blade element theory to calculate the lift and drag forces (that produce thrust, rotor shaft torque and power) based on the local simulated flow and the blade characteristics. In order to predict simultaneously the turbine angular velocity and the turbine-induced forces (and thus the power output), a new iterative dynamic procedure is developed to couple the ADM-R turbine model with a relationship between shaft torque and rotational speed. This relationship, which is unique for a given turbine model and independent of the inflow condition, is derived from simulations of a stand-alone wind turbine in conditions for which the thrust coefficient can be validated. Comparison with observed power data from the Horns Rev wind farm shows that better power predictions are obtained with the dynamic ADM-R than with the standard ADM, which assumes a uniform thrust distribution and ignores the torque effect on the turbine wakes and rotor power. The results are also compared with the power predictions obtained using two commercial wind-farm design tools (WindSim and WAsP). These models are found to underestimate the power output compared with the results from the proposed LES framework.     

海上风力发电机组基础的选择

介绍了海上风力发电的发展现状,结合海上采油平台形式,对海上风电机组采用的基础定义、基础类型及其选择进行了介绍。     

Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: a composite‐based analysis with the Weather Research and Forecasting model over Horns Rev

The use of mesoscale modeling to reproduce the power deficits associated with wind turbine wakes in an offshore environment is analyzed. The study is based on multiyear (3 years) observational and modeling results at the Horns Rev wind farm. The simulations are performed with the Weather Research and Forecasting mesoscale model configured at a high horizontal resolution of 333 m over Horns Rev. The wind turbines are represented as an elevated momentum sink and a source of turbulent kinetic energy. Composites with different atmospheric conditions are extracted from both the observed and simulated datasets in order to inspect the ability of the model to reproduce the power deficit in a wide range of atmospheric conditions. Results indicate that mesoscale models such as Weather Research and Forecasting are able to qualitatively reproduce the power deficit at the wind farm scale. Some specific differences are identified. Mesoscale modeling is therefore a suitable framework to analyze potential downstream effects associated with offshore wind farms. Copyright © 2014 John Wiley & Sons, Ltd.     

Adjustment of wind farm power output through flexible turbine operation using wind farm control

When the installed capacity of wind power becomes high, the power generated by wind farms can no longer simply be that dictated by the wind speed. With sufficiently high penetration, it will be necessary for wind farms to provide assistance with supply‐demand matching. The work presented here introduces a wind farm controller that regulates the power generated by the wind farm to match the grid requirements by causing the power generated by each turbine to be adjusted. Further, benefits include fast response to reach the wind farm power demanded, flexibility, little fluctuation in the wind farm power output and provision of synthetic inertia. Copyright © 2015 John Wiley & Sons, Ltd.     

Survey of modelling methods for wind turbine wakes and wind farms

This article provides an overview and analysis of different wake‐modelling methods which may be used as prediction and design tools for both wind turbines and wind farms. We also survey the available data concerning the measurement of wind magnitudes in both single wakes and wind farms, and of loading effects on wind turbines under single‐ and multiple‐wake conditions. The relative merits of existing wake and wind farm models and their ability to reproduce experimental results are discussed. Conclusions are provided concerning the usefulness of the different modelling approaches examined, and difficult issues which have not yet been satisfactorily treated and which require further research are discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

大连近海风电场风机机组的选型与布置初探

风机的选型与布置是风电场建设可行性研究的重要内容,对风电场的建设造价和投产后的发电效益有重要的影响。文章在大连近海风资源评估的基础上,综合考虑国内外风力发电机组的制造水平、技术成熟程度,选择4种机型,布置在两个参考场址,预测其理论发电量,通过技术经济比较,选出最佳机型。一号场址的水文地质条件比较利于风机布置,可安装34台单机容量为3 MW的风力发电机组,布置方式为2排17列,年上网发电量约为26 262万kWh。     

Effects of inflow turbulence intensity and turbine arrangements on the power generation efficiency of large wind farms

In this study, we conduct a series of large‐eddy simulations (LESs) to study the impact of different incoming turbulent boundary layer flows over large wind farms, with a particular focus on the overall efficiency of electricity production and the evolution of the turbine wake structure. Five representative turbine placements in the large wind farm are considered, including an aligned layout and four staggered layouts with lateral or vertical offset arrangements. Four incoming flow conditions are used and arranged from the LESs of the ABL flow over homogeneous flat surfaces with four different aerodynamic roughness lengths (i.e., z0 = 0.5, 0.1, 0.01, and 0.0001 m), where the hub‐height turbulence intensity levels are about 11.1%, 8.9%, 6.8%, and 4.9%, respectively. The simulation results indicate that an enhancement in the inflow turbulence level can effectively increase the power generation efficiency in the large wind farms, with about 23.3% increment on the overall farm power production and up to about 32.0% increment on the downstream turbine power production. Under the same inflow condition, the change of the turbine‐array layouts can increase power outputs within the first 10 turbine rows, which has a maximum increment of about 26.5% under the inflow condition with low turbulence. By comparison, the increase of the inflow turbulence intensity facilitates faster wake recovery that raises the power generation efficiency of large wind farms than the adjustment of the turbine placing layouts.     

新型海上风力发电及其关键技术研究

回顾国外海上风力发电场的发展,针对随着海水深度增加导致海上风力机成本急剧上升的矛盾,引入海上漂浮式风力机概念,并详细介绍其结构和特点,通过系统介绍海上漂浮式风力机组成部分和设计制造中的关键技术,最后得出海上漂浮式风机是一种潜力巨大的新型风力发电技术,值得进一步深入研究。同时,针对我国陆、海资源的具体情况,较为系统地提出了海上漂浮式风力机研究的需要关注的关键问题,指出了该研究所具有的巨大社会经济价值。     

Quantification of power losses due to wind turbine wake interactions through SCADA,meteorological and wind LiDAR data

Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.     

An assessment of the impact of reduced averaging time on small wind turbine power curves,energy capture predictions and turbulence intensity measurements

The effect of varying the averaging time of measured data used to calculate wind turbine power curves is examined. The effects of reducing the averaging time from 10 to 1 min, as recommended for small wind turbines, are investigated using power performance data recorded using a 15 kW wind turbine. Test site data have been processed according to the relevant international standard, IEC 61400‐12‐1, to provide power curves and annual energy yield predictions. A number of issues are explored: the systematic distortion of the power curve that occurs as averaging time is decreased, the errors introduced by the use of 1 min averaged power curves to calculate energy yield and the reduction of turbulence intensity as averaging time is reduced. Recommendations for improved small wind turbine testing and energy yield calculation are given. Copyright © 2012 John Wiley & Sons, Ltd.     

风电场风电机组的接地设计

较系统地介绍了风电场风电机组对接地电阻的要求、接地设计思路及方法,并提供实际工程中接地网布置图实例作为参考。     

Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm

Accurately quantifying wind turbine wakes is a key aspect of wind farm economics in large wind farms. This paper introduces a new simulation post‐processing method to address the wind direction uncertainty present in the measurements of the Horns Rev offshore wind farm. This new technique replaces the traditional simulations performed with the 10 min average wind direction by a weighted average of several simulations covering a wide span of directions. The weights are based on a normal distribution to account for the uncertainty from the yaw misalignment of the reference turbine, the spatial variability of the wind direction inside the wind farm and the variability of the wind direction within the averaging period. The results show that the technique corrects the predictions of the models when the simulations and data are averaged over narrow wind direction sectors. In addition, the agreement of the shape of the power deficit in a single wake situation is improved. The robustness of the method is verified using the Jensen model, the Larsen model and Fuga, which are three different engineering wake models. The results indicate that the discrepancies between the traditional numerical simulations and power production data for narrow wind direction sectors are not caused by an inherent inaccuracy of the current wake models, but rather by the large wind direction uncertainty included in the dataset. The technique can potentially improve wind farm control algorithms and layout optimization because both applications require accurate wake predictions for narrow wind direction sectors. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Synthetic turbulence modelling for offshore wind farm engineering models using coherence aggregation

Turbulent wind fields are known to be a major driver for structural loads and power fluctuations on offshore wind turbines. At the single-turbine scale, there exist well-established design standards based on wind spectra and coherence functions calibrated from years of measurements, which are used to generate multiple 10-min wind field realisations known as synthetic turbulence boxes, themselves used as input to turbine-scale aero-hydro-servo elastic codes. These methods are however not directly applicable at farm scale. When analysing the dynamics of large offshore wind farms, measurements reveal the importance of large, low-frequency turbulent vortices for power fluctuations and hence for wind farm control and grid integration. Also, farm-scale wind fields are needed as input to farm-scale aero-servo-elastic codes for the modelling of wake dynamics, affecting structural loads. These new concerns motivate an upgrade in the original turbine-scale wind field representation: (1) spectral models need to be based on farm-scale measurements, (2) the frozen-turbulence assumption merging temporal and along-wind coherence must be lifted, (3) simplifications are needed to reduce the number of degrees of freedom as the domain becomes excessively large. This paper suggests models and algorithms for aggregated farm-wide corrrelated synthetic turbulence generation—lumping the wind field into space-averaged quantities—adapted to the aero-hydro-servo elastic modelling of large offshore wind farms. Starting from the work of Sørensen et al. in the early 2000s for grid integration purposes, methods for structural load modelling (through wake meandering and high-resolution wind field reconstruction) are introduced. Implementation and efficiency matters involving mathematical subtleties are then presented. Finally, numerical experiments are carried out to (1) verify the approach and implementation against a state-of-the-art point-based—as opposite to aggregated—synthetic turbulence generation code and (2) illustrate the benefit of turbulence aggregation for the modelling of large offshore wind farms.     

Experimental verification of computational predictions in power generation variation with layout of offshore wind farms

The optimization of wind farms with respect to spatial layout is addressed experimentally. Wake effects within wind turbine farms are well known to be deleterious in terms of power generation and structural loading, which is corroborated in this study. Computational models are the predominant tools in the prediction of turbine‐induced flow fields. However, for wind farms comprising hundreds of turbines, reliability of the obtained numerical data becomes a growing concern with potentially costly consequences. This study pursues a systematic complementary theoretical, experimental and numerical study of variations in generated power with turbine layout of an 80 turbine large wind farm. Wake effects within offshore wind turbine arrays are emulated using porous discs mounted on a flat plate in a wind tunnel. The adopted approach to reproduce experimentally individual turbine wake characteristics is presented, and drag measurements are argued to correctly capture the variation in power generation with turbine layout. Experimental data are juxtaposed with power predictions using ANSYS WindModeller simulation suite. Although comparison with available wind farm power output data has been limited, it is demonstrated nonetheless that this approach has potential for the validation of numerical models of power loss due to wake effects or even to make a direct physical prediction. The approach has even indicated useful data for the improvement of the physics within numerical models. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

Contributions to wind farm power estimation considering wind direction‐dependent wake effects

This paper presents a contribution to wind farm ouput power estimation. The calculation for a single wind turbine involves the use of the power coefficient or, more directly, the power curve data sheet. Thus, if the wind speed value is given, a simple calculation or search in the data sheet will provide the generated power as a result. However, a wind farm generally comprises more than one wind turbine, which means the estimation of power generated by the wind farm as a function of the wind speed is a more complex process that depends on several factors, including the important issue of wind direction. While the concept of a wind turbine power curve for a single wind turbine is clear, it is more subject to discussion when applied to a whole wind farm. This paper provides a simplified method for the estimation of wind farm power, based on the use of an equivalent wake effect coefficient. Copyright © 2016 John Wiley & Sons, Ltd.     

风电场风机初步选型方法研究

市场上可供选择的风机种类众多,各类风机在性能、价格、维修及制造商服务等多方面都存在明显差异,因此在选择风机时有必要综合考虑多方面因素。鉴于工程上对风机作初步选型时采用的方法大多具有局限性,文章在确定影响风机选型的主要因素后提出一套风机初步选型方法,该方法根据拟建风电场的情况和设计人员的要求,综合考虑各因素后选出符合条件的风机。文章基于该选型方法将Visual Basic语言与Microsoft Access数据库相结合,开发出相应的选型系统,使整个选型过程更为方便、快捷。     

Wind tunnel study on wind and turbulence intensity profiles in wind turbine wake

In recent years, there has been a rapid development of the wind farms in Japan. It becomes very important to investigate the wind turbine arrangement in wind farm, in order that the wake of one wind turbine does not to interfere with the flow in other wind turbines. In such a case, in order to achieve the highest possible efficiency from the wind, and to install as many as possible wind turbines within a limited area, it becomes a necessity to study the mutual interference of the wake developed by wind turbines. However, there is no report related to the effect of the turbulence intensity of the external flow on the wake behind a wind turbine generated in the wind tunnel. In this paper, the measurement results of the averaged wind profile and turbulence intensity profile in the wake in the wind tunnel are shown when the turbulence intensity of the external wind was changed. The wind tunnel experiment is performed with 500mm-diameter two-bladed horizontal axis wind turbine and the wind velocity in wake is measured by an I-type hot wire probe. As a result, it is clarified that high turbulence intensities enable to the entrainment of the main flow and the wake and to recover quickly the velocity in the wake.