偏航工况下风电机组尾流模型与风电场尾流叠加研究

风电场中通过风电机组主动偏航进行尾流优化控制,可以提高风电场发电量。文中根据偏航工况对风电机组尾流和功率输出的影响,建立了偏航工况下单台风电机组尾流模型和输出功率的简化计算方法。而对于全场不同来流风向,对机组位置进行坐标变换以确定风电机组的迎风顺序,并结合尾流叠加模型建立了偏航工况下风电场尾流分布计算方法。最后,以单列6台风电机组为研究对象进行计算分析,验证了该尾流计算方法的适用性及主动偏航控制对风电场发电量提升的可行性。     

基于场级偏航控制的风电场尾流敏感性分析

偏航偏转控制有利于减小风机尾流效应,通过场级偏航协调优化减小尾流损失,可使风电场总发电量达到最大化。采用FLORIS尾流代理模型,以各风机偏航角为优化对象,风电场总功率最大为优化目标,进行场级偏航寻优。针对不同风机间距、纵列个数、湍流强度、来流风速和来流风向等多个维度,对比分析了偏航优化对尾流损失及功率提升的敏感性。结果表明：当风电场排布间距小于5D、风机纵列大于3台且仅需优化前5排、纵列机位连线与风玫瑰图主频风向夹角小于15°、风场湍流小于0.1、来流风速位于风机“切入风速+2 m/s”至“额定风速+2 m/s”区间时,场级偏航控制对于尾流优化效果最佳;若仅采用单机偏航控制风向,前排风机保留3°～5°偏航误差有利于风电场整体的发电收益。     

考虑尾流效应的风电场优化控制技术研究

针对已建风电场,提出一种考虑尾流效应的风电场优化控制方法,以减少风电场尾流效应,提高风电场整体输出功率。研究机组状态参数变化与输出功率、尾流分布间的量化关系,揭示风电机组状态参数变化与输出功率、尾流分布间的耦合关系;提出尾流与风轮交汇面积的计算方法,建立多台风电机组的尾流叠加模型;以风电场整体输出功率最大为目标函数,轴向诱导因子为优化参数,粒子群算法为优化算法,建立考虑尾流效应的风电场优化控制模型。以丹麦Horns Rev风电场为算例进行计算分析,结果表明:所提出的考虑尾流效应的风电场优化控制方法能够使风电场整体输出功率增加。     

考虑疲劳均衡的海上风电场主动尾流控制研究

海上风电场运行维护成本高,而其尾流效应影响更加突出,不但会影响风电场的发电效率,还会增大风电场内机组的疲劳载荷,增加运维成本。文章针对基于疲劳均匀的海上风电场主动尾流控制展开研究,通过GH-Bladed软件计算建立了风电机组在典型控制工况下关键零部件的疲劳损伤量数据库。其中的工况包括最大功率追踪、桨距角控制和偏航控制3种,并引用了量子粒子群算法,通过变桨和偏航两种方法进行优化控制,以实现海上风电场发电量提升和风电机组疲劳均匀的多目标主动尾流优化控制策略,降低海上风电场运维成本。仿真结果表明了所提出控制方法的可行性。     

基于主动尾流控制的风电机群协同优化调度

针对机组间尾流效应严重影响风电机组发电效率的问题,提出了风电机组安全偏航约束计算方法、尾流特性混合半机理建模方法以及风电机群多目标协同优化调度方法。基于FAST. FARM平台完善了多自由度可控机组与尾流的动态交互集成仿真环境,对比分析了2台机组串列式排布以及华东地区某海上风电场7台机组实际排布下的协同运行优化性能。结果表明：所建立的集成仿真模型能够合理表征风电机群与空气流场的多领域动态交互特性,所提方法能够有效提升风电机群发电效能,促进经济效益、资源利用和成本控制的均衡优化。     

海上风电场功率提升和疲劳平衡综合优化控制

针对海上风电场,综合功率提升和疲劳平衡分配的优化目标,提出一种以天为优化周期的优化策略。在电网高负荷时段,基于Jensen尾流模型,以轴向诱导因子为优化变量,风电场整场功率最大为目标,运用随机粒子群算法进行风功率利用提升优化控制;在电网低负荷时段,基于风电机组综合疲劳系数计算方法,以机组轴向诱导因子为优化变量,应用尾流计算模型调整轴向诱导因子来满足电网限功率指令,以机组疲劳系数标准差最小为目标,采用粒子群算法寻优进行疲劳平衡优化。以某海上风电场进行算例分析,结果表明该优化策略在一天的优化周期内可较好地实现风电场功率提升和疲劳平衡的综合优化。     

考虑尾流效应的风电场有功功率控制策略研究

随着大型风电场的快速发展,由于尾流效应造成的风电场能量损失成为重要的问题。本文考虑风电场内的尾流效应,提出了优化的有功功率和桨距角曲线以降低独立机组的能量损失,从而达到风电场的总有功功率提升的目的。同时,通过挖掘风电机组有功出力和尾流效应的关系,给出基于有功控制的尾流优化方法,建立了风电场有功出力优化模型。最后,基于某风电场的实际数据建立仿真模型来检验控制策略的有效性,并引入传统单机MPPT方案进行比对,结果证明提出的新型控制策略大大提高了整个风电场的有功功率,并且计算量小,优化方法简单,具有一定的工程应用价值。     

计及风向不确定性的海上风电场年发电量计算

以欧洲Lillgrund海上风电场为例,建立基于Larsen尾流模型及线性叠加模型的风电场输出功率及发电量计算模型;考虑风电机组偏航偏差等风向不确定性的影响,建立基于高斯平均方法的风电场计算功率修正模型;结合风电场实测数据及发电量计算收敛过程分析,研究了修正模型对风电场功率及发电量计算的影响。结果表明,所建立的尾流作用下的风电场功率计算模型能够较好地反应实际风电场的尾流影响特征,高斯平均修正方法进一步提高了尾流作用下风电场功率计算精度,并提高了发电量计算的收敛速度。在风电场年发电量计算中考虑风向不确定性的影响,对于提高模型评估与验证的准确性具有重要意义。     

基于非线性预测控制的风电场功率提升控制

随着大型风电场的快速发展,减小尾流效应造成的风电场能量损失成为研究热点之一。针对风电场实际运行中的风速变化以及尾流延时问题,基于尾流延时模型,建立考虑时间变量的风电场功率预测模型。以风电场输出功率最大化为目标,设计了非线性预测控制器,该控制器采用非线性预测模型,并采用PSO算法对预测时域内的性能指标进行优化,得到各台风机的控制值。基于Sim Wind Farm软件对该控制策略进行验证,并与传统风电场控制策略进行仿真比较,结果表明,这种新控制策略可以有效提升风电场的总体功率。     

可用于风电场优化控制的尾流快速计算方法

根据空气动力学原理推导单台风电机组尾流模型,建立多台风电机组的尾流叠加模型。在此基础上,提出一种全场尾流快速计算方法,该方法用经过坐标原点的直线来表示不同的来流风向,计算各风电机组沿此直线方向到坐标原点的距离,以此距离确定风电机组的迎风顺序,根据风电机组单台尾流模型、尾流叠加模型及风电机组的迎风顺序,计算整个风电场的尾流分布。以丹麦Horns Rev风电场为算例进行计算分析,结果表明,该文所述的尾流快速计算方法能准确、快速计算风电场尾流分布。     

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.     

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.     

Global sensitivity analysis of wind turbine power output

The dynamics of wind turbine behavior are complex and a critical area of study for the wind industry. Identification of factors that cause changes in turbine performance can sometimes prove to be challenging, whereas other times, it can be intuitive. The quantification of the effect that these factors have is valuable for making improvements to both power performance and turbine health. In commercial farms, large quantities of meteorological and performance data are commonly collected to monitor daily operations. These data can also be used to analyze the relationship between each parameter in order to better understand the interactions that occur and the information contained within these signals. In this global sensitivity analysis, a neural network is used to model select wind turbine supervisory control and data acquisition system parameters for an array of turbines from a commercial wind farm that exhibit signs of wake interaction. An extended Fourier amplitude sensitivity test is then performed for 2 years of 10‐min averaged data. The study examines the primary and combined sensitivities of power output to each selected parameter for two turbines in the array. The primary sensitivities correspond to single parameter interactions, whereas combined sensitivities account for interactions between multiple parameters simultaneously. Highly influential parameters such as wind speed and rotor rotation frequency produce expected results; the extended Fourier amplitude sensitivity test method proved effective at quantifying the sensitivity of a wide range of more subtle inputs. These include blade pitch, yaw position, main bearing and ambient temperatures as well as wind speed and yaw position standard deviation. The technique holds promise for application in full‐scale wake studies where it might be used to determine the benefits of emerging power optimization strategies such as active wake management. The field of structural health monitoring can also benefit from this method. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects called the FLOw Redirection and Induction in Steady‐state (FLORIS) model. The FLORIS model predicts the steady‐state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high‐fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

An experimental investigation on the wake interferences among wind turbines sited in aligned and staggered wind farms

An experimental investigation was conducted for a better understanding of the wake interferences among wind turbines sited in wind farms with different turbine layout designs. Two different types of inflows were generated in an atmospheric boundary layer wind tunnel to simulate the different incoming surface winds over typical onshore and offshore wind farms. In addition to quantifying the power outputs and dynamic wind loads acting on the model turbines, the characteristics of the wake flows inside the wind farms were also examined quantitatively. After adding turbines staggered between the first 2 rows of an aligned wind farm to increase the turbine number density in the wind farm, the added staggered turbines did not show a significant effect on the aeromechanical performance of the downstream turbines for the offshore case. However, for the onshore case, while the upstream staggered turbines have a beneficial effect on the power outputs of the downstream turbines, the fatigue loads acting on the downstream turbines were also found to increase considerably due to the wake effects induced by the upstream turbines. With the same turbine number density and same inflow characteristics, the wind turbines were found to be able to generate much more power when they are arranged in a staggered layout than those in an aligned layout. In addition, the characteristics of the dynamic wind loads acting on the wind turbines sited in the aligned layout, including the fluctuation amplitudes and power spectrum, were found to be significantly different from those with staggered layout.     

Maximization of the annual energy production of wind power plants by optimization of layout and yaw‐based wake control

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw‐based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady‐state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above‐rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.     

Maximum power‐point tracking control for wind farms

This paper presents a data‐driven adaptive scheme to adjust the control settings of each wind turbine in a wind farm such that an increase in the total power production of the wind farm is achieved. This is carried out by taking into account the interaction between the turbines through wake effects. The optimization scheme is designed in such a way that it yields fast convergence so that it can adapt to changing wind conditions quickly. The scheme has a distributed architecture in which each wind turbine adapts its control settings through gradient‐based optimization, using information that it receives from neighbouring turbines. The novel control method is tested in a simulation of the Princess Amalia Wind Park. It is shown that the distributed gradient‐based approach performs the optimization in a more time‐efficient manner compared with an existing data‐driven wind farm power optimization method that uses a game theoretic approach. Copyright © 2014 John Wiley & Sons, Ltd.