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.     

Active wake control: An approach to optimize the lifetime operation of wind farms

The wind turbines within a wind farm impact each other's power production and loads through their wakes. Wake control strategies, aiming to reduce wake effects, receive increasing interest by both the research community and the industry. A number of recent simulation studies with high fidelity wake models indicate that wake mitigation control is a very promising concept for increasing the power production of a wind farm and/or reducing the fatigue loading on wind turbines' components. The purpose of this paper is to study the benefits of wake mitigation control in terms of lifetime power production and fatigue loading on several existing full‐scale commercial wind farms with different scale, layouts, and turbine sizes. For modeling the wake interactions, Energy Research Centre of the Netherlands' FarmFlow software is used: a 3D parabolized Navier‐Stokes code, including a k‐? turbulence model. In addition, an optimization approach is proposed that maximizes the lifetime power production, thereby incorporating the fatigue loads into the optimization criterion in terms of a lifetime extension factor.     

Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm

This paper investigates wake effects on load and power production by using the dynamic wake meander (DWM) model implemented in the aeroelastic code HAWC2. The instationary wind farm flow characteristics are modeled by treating the wind turbine wakes as passive tracers transported downstream using a meandering process driven by the low frequent cross‐wind turbulence components. The model complex is validated by comparing simulated and measured loads for the Dutch Egmond aan Zee wind farm consisting of 36 Vestas V90 turbine located outside the coast of the Netherlands. Loads and production are compared for two distinct wind directions—a free wind situation from the dominating southwest and a full wake situation from northwest, where the observed turbine is operating in wake from five turbines in a row with 7D spacing. The measurements have a very high quality, allowing for detailed comparison of both fatigue and min–mean–max loads for blade root flap, tower yaw and tower bottom bending moments, respectively. Since the observed turbine is located deep inside a row of turbines, a new method on how to handle multiple wakes interaction is proposed. The agreement between measurements and simulations is excellent regarding power production in both free and wake sector, and a very good agreement is seen for the load comparisons too. This enables the conclusion that wake meandering, caused by large scale ambient turbulence, is indeed an important contribution to wake loading in wind farms. Copyright © 2012 John Wiley & Sons, Ltd.     

Validation of four LES and a vortex model against stereo-PIV measurements in the near wake of an actuator disc and a wind turbine

In this paper we report the results of a workshop organised by the Delft University of Technology in 2014, aiming at the comparison between different state-of-the-art numerical models for the simulation of wind turbine wakes. The chosen benchmark case is a wind tunnel measurement, where stereoscopic Particle Image Velocimetry was employed to obtain the velocity field and turbulence statistics in the near wake of a two-bladed wind turbine model and of a porous disc, which mimics the numerical actuator used in the simulations. Researchers have been invited to simulate the experimental case based on the disc drag coefficient and the inflow characteristics. Four large eddy simulation (LES) codes from different institutions and a vortex model are part of the comparison. The purpose of this benchmark is to validate the numerical predictions of the flow field statistics in the near wake of an actuator disc, a case that is highly relevant for full wind farm applications. The comparison has shown that, despite its extreme simplicity, the vortex model is capable of reproducing the wake expansion and the centreline velocity with very high accuracy. Also all tested LES models are able to predict the velocity deficit in the very near wake well, contrary to what was expected from previous literature. However, the resolved velocity fluctuations in the LES are below the experimentally measured values.     

A novel control strategy approach to optimally design a wind farm layout

Recently wind energy has become one of the most important alternative energy sources and is growing at a rapid rate because of its renewability and abundancy. For the clustered wind turbines in a wind farm, significant wind power losses have been observed due to wake interactions of the air flow induced by the upstream turbines to the downstream turbines. One approach to reduce power losses caused by the wake interactions is through the optimization of wind farm layout, which determine the wind turbine positions and control strategy, which determine the wind turbine operations. In this paper, a new approach named simultaneous layout plus control optimization is developed. The effectiveness is studied by comparison to two other approaches (layout optimization and control optimization). The results of different optimizations, using both grid based and unrestricted coordinate wind farm design methods, are compared for both ideal and realistic wind conditions. Even though the simultaneous layout plus control optimization is theoretically superior to the others, it is prone to the local minima. Through the parametric study of crossover and mutation probabilities of the optimization algorithm, the results of the approach are generally satisfactory. For both simple and realistic wind conditions, the wind farm with the optimized control strategy yield 1–3 kW more power per turbine than that with the self-optimum control strategy, and the unrestricted coordinate method yield 1–2 kW more power per turbine than the grid based method.     

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.     

Analytical model for predicting the performance of arbitrary size and layout wind farms

A simple engineering model for predicting wind farm performance is presented, which is applicable to wind farms of arbitrary size and turbine layout. For modeling the interaction of wind farm with the atmospheric boundary layer (ABL), the wind farm is represented as added roughness elements. The wind speed behind each turbine is calculated using a kinematic model, in which the friction velocity and the wind speed outside the turbine wake, constructed based on the wind farm‐ABL interaction model, are employed to estimate the wake expansion rate in the crosswind direction and the maximum wind speed that can be recovered within the turbine wake, respectively. Validation of the model is carried out by comparing the model predictions with the measurements from wind tunnel experiments and the Horns Rev wind farm. For all validation cases, satisfactory agreement is obtained between model predictions and experimental data. Copyright © 2015 John Wiley & Sons, Ltd.     

Influence of wake dynamics on the performance and aeroelasticity of wind turbines

Wind turbines are currently a rapidly expanding form of renewable energy. However, there are numerous technological challenges that must be overcome before wind energy provides a significant amount of power in the United States. One of the primary challenges in wind turbine design and analysis is accurately accounting for the aerodynamic environment. This study is focused on a comprehensive verification and validation of the NREL FAST code, which is enhanced to include a free vortex wake model. The verification and validation is carried out through a comparison of blade lift distribution, wind turbine power and force and moment coefficients using a combination of CFD and experimental data. The results are also compared against Blade Element Momentum theory, and results from a 2001 double-blind NREL study on the prediction capabilities of wind turbine modeling tools. Results indicate that the enhanced aeroelastic code generally provides improved predictions. However, in several notable cases the predictions are only marginally improved, or even worse, than those generated using Blade Element Momentum theory aerodynamics. It is concluded that modeling of the aerodynamic environment remains incomplete, even after inclusion of wake effects. One important aspect identified is modeling of the unsteady aerodynamic lift characteristics of the rotor. Finally, the aeroelastic response in the combined presence of wake effects and inflow turbulence is examined. Significant differences are observed in loads, power, and structural response between results computed using the free wake model or simpler models, such as Blade Element Momentum theory.     

基于偏航尾流模型的风电场功率协同优化研究

为减小风电场尾流效应的影响,提升风电场整体发电量,提出一种基于偏航尾流模型的风电场功率协同优化方法。首先建立风电场偏航尾流模型,该模型包括用于计算单机组尾流速度分布的Jensen-Gaussian尾流模型、尾流偏转模型及多机组尾流叠加模型,对各机组风轮前来流风速进行求解;再根据来流风速计算风电场输出功率,并以风电场整体输出功率最大为优化目标,利用拟牛顿算法协同优化各机组轴向诱导因子和偏航角度。以4行4列方形布置的16台NREL-5 MW风电机组为对象进行仿真研究。结果表明,所提出的基于偏航尾流模型的风电场功率协同优化方法能显著提升风电场整体输出功率。     

Wake impacts on downstream wind turbine performance and yaw alignment

Aerodynamic wake interaction between commercial scale wind turbines can be a significant source of power losses and increased fatigue loads across a wind farm. Significant research has been dedicated to the study of wind turbine wakes and wake model development. This paper profiles influential wake regions for an onshore wind farm using 6 months of recorded SCADA (supervisory control and data acquisition) data. An average wind velocity deficit of over 30% was observed corresponding to power coefficient losses of 0.2 in the wake region. Wind speed fluctuations are also quantified for an array of turbines, inferring an increase in turbulence within the wake region. A study of yaw data within the array showed turbine nacelle misalignment under a range of downstream wake angles, indicating a characteristic of wind turbine behaviour not generally considered in wake studies. The turbines yaw independently in order to capture the increased wind speeds present due to the lateral influx of turbulent wind, contrary to many experimental and simulation methods found in the literature. Improvements are suggested for wind farm control strategies that may improve farm‐wide power output. Additionally, possible causes for wind farm wake model overestimation of wake losses are proposed.Copyright © 2012 John Wiley & Sons, Ltd.     

考虑尾流效应的风电机组启停优化研究

以某典型风电场为例,采用尾流模型模拟研究风电机组启停优化对风电机组尾流干涉和发电量的影响。在速度恢复系数小于0.06时,典型机位的停机可增加风电场全场发电量。以中国北方某实际风电场为例进行现场试验,在主风向下,通过调度上游风电机组的启停,实现区域内风电机组发电量提升,验证方法的有效性。     

格尼襟翼垂直轴风力机风场研究

为研究垂直轴风力机风场中机组气动性能受格尼襟翼的影响,采用TSST湍流模型对直线翼垂直轴风力机进行数值模拟研究.结果表明:风场上游风力机组尖速比越大,机组间流体加速效果越显著,使风力机组气动性能高于单风力机;在中低尖速比时,格尼襟翼可有效提升单个风力机气动效率,在尖速比较高时,提升效果并不明显;在风力机组中安装格尼襟翼...     

Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment

This study examines the effect of different wind turbine classes on the electricity production of wind farms in three areas of Australia, which present low, low to medium, and medium to high wind potential: Gingin, Armidale, and Gold Coast Seaway. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from six different manufacturers have been used. For each manufacturer, at lest two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC I, IEC II and/or IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (higher IEC class) in all three locations, in terms of energy production. This improvement is higher for the locations with lower and medium wind potential (Gingin and Armidale), and varies from 5% to 55%. Moreover, this study investigates the economical feasibility of a 30 MW wind farm, for all combinations of site locations and wind turbine models.     

On the wake deflection of vertical axis wind turbines by pitched blades

Wake losses are a critical consideration in wind farm design. The ability to steer and deform wakes can result in increased wind farm power density and reduced energy costs and can be used to optimize wind farm designs. This study investigates the wake deflection of a vertical axis wind turbine (VAWT) experimentally, emphasizing the effect of different load distributions on the wake convection and mixing. A trailing vortex system responsible for the wake topology is hypothesized based on a simplified vorticity equation that describes the relationship between load distribution and its vortex generation; the proposed vorticity system and the resulting wake topology are experimentally validated in the wind tunnel via stereoscopic particle image velocimetry measurements of the flow field at several wake cross-sections. Variations in load distribution are accomplished by a set of fixed blade pitches. The experimental results not only validate the predicted vorticity system but also highlight the critical role of the streamwise vorticity component in the deflection and deformation of the wake, thus affecting the momentum and energy recoveries. The evaluation of the various loading cases demonstrates the significant effect of the wake deflection on the wind power available to a downwind turbine, even when the distance between the two turbines is only three diameters.     

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.     

Centralised power control of wind farm with doubly fed induction generators

At the moment, the control ability of wind farms is a prime research concern for the grid integration of large wind farms, due to their required active role in the power system. This paper describes the on-going work of a research project, whose overall objective is to analyse and assess the possibilities for control of different wind farm concepts. The scope of this paper is the control of a wind farm made up exclusively of doubly fed induction generators. The paper addresses the design and implementation issues of such a controller and focuses on the ability of the wind farm control strategy to regulate the wind farm power production to the reference power ordered by the system operators. The presented wind farm control has a hierarchical structure with both a central control level and a local control level. The central wind farm control level controls the power production of the whole farm by sending out reference power signals to each individual wind turbine, while the local wind turbine control level ensures that the reference power signal send by the central control level is reached. The performance of the control strategy is assessed and discussed by means of simulations illustrated both at the wind farm level and at each individual wind turbine level.     

Wake effect in wind farm performance: Steady-state and dynamic behavior

The aim of this paper is to evaluate the impact of the wake effect on both the steady-state operation and dynamic performance of a wind farm and provide conclusions that can be used as thumb rules in generic assessments where the full details of the wind farms are unknown. A simplified explicit model of the wake effect is presented, which includes: the cumulative impact of multiple shadowing, the effects of wind direction and the wind speed time delay. The model is implemented in MATLAB^® and then integrated into a power system simulation package to describe the wake effect and its impact on a wind farm, particularly in terms of the wake coefficient and overall active power losses. Results for two wind farm layouts are presented to illustrate the importance of wind turbine spacing and the directionality of wind speeds when assessing the wake effect during steady-state operation and dynamic behavior.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

In the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a second-stage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.     

Wind farm layout optimization using area dimensions and definite point selection techniques

Wind turbines are the biggest rotating machines on earth, operating in the lowest part of the earth boundary layer. Designing the layout scheme of wind farms is a challenging job to researchers, as there are many design objectives and constraints due to the multiple wake phenomena. This paper proposes an area rotation method to find the optimum dimensions of the wind farm shape, where maximum area could face the free stream velocity. Afterwards, a novel method called Definite Point Selection (DPS) is developed to place the turbines in order to operate at maximum, while providing obligatory space between adjacent turbines for operation safety. This method can be used to identify the zero wake effect points at wind farm. The result from this study shows that the proposed method is more effective to increase the overall power of a wind farm than the previous methods. Also, the power output of the wind farm by using combined area rotation and DPS methods was increased even when using the same number of wind turbines.