考虑尾流效应的风电场输出功率优化

针对尾流效应对风电场输出功率造成的损失,文章提出了一种基于改进Jensen模型的优化方法。基于激光雷达实验数据验证了改进Jensen模型的有效性,并建立了多机组尾流叠加模型。对考虑尾流效应的风电场输出功率优化可行性进行分析,建立了风电场输出功率模型。针对标准粒子群算法过早收敛、易局部最优的缺陷进行了改进,在其迭代方程中加入二阶振荡环节,增加了粒子的多样性,提高了算法的全局搜索能力,同时保证了算法的运行速度;引入模拟退火操作,增强了算法的局部搜索能力。建立了风电场输出功率最大化优化模型,以轴向诱导因子为优化参数,利用改进粒子群算法对山西省某风电场模型进行了仿真分析。结果表明：当入流风速分别为8 m/s和12 m/s时,经改进粒子群算法优化之后,风电场输出功率分别提高了6.26%和4.59%;改进粒子群算法改善了标准粒子群算法存在的过早收敛、易局部最优的缺陷。     

基于发电成本和疲劳均匀性的风电场有功功率控制策略研究

针对限功率工况下风电场机组有功分配问题,文章建立了机组发电成本模型和综合疲劳模型。采用改进的粒子群优化算法,在风电场各机组风功率预测信息的基础上,以风电机组疲劳均衡、单位发电成本最低为目标,以功率平衡、出力限制、开停机时间限制等为约束条件,研究限电情况下风电场内有功控制优化策略。最后,对比3种分配方案下风电场的有功总出力、发电成本和疲劳均匀性,分析限功率控制下风电场典型机组的有功出力特征,结果表明,同时考虑发电成本和疲劳均衡条件的优化策略在有效降低成本的基础上能够保证机组间的疲劳均衡,从而降低机组维修频率。     

风电场的输出功率特性研究

研究了风电场的输出功率特性，提出了尾流系数矩阵，功率特性矩阵以及等效输出功率特性等概念和相应的算法。     

海上相邻风电场间的“尾流效应”实例分析

目的  研究海上相邻风电场间的“尾流效应”对发电损失的影响。 方法  利用海上风电场实际运行SCADA数据结合激光雷达同期实测测风数据,基于不同的风向扇区范围和风电场实际排布进行尾流效应场景分类,开展实际运行相邻风电场间（20D以上间距）的真实尾流电量损失分析工作。 结果  结果表明：对于规则排布的海上大型风电场,基于实际运行SCADA数据,对各机组发电量进行归一化,可以较好地反映海上风能资源分布特征及各机组发电能力的差异;高度集中的单一扇区条件下,处于下风向的相邻风电场受上风向相邻场区的“尾流效应”影响明显,发电产能较自由流降幅明显;相邻风场间随着缓冲带距离的增加,下风向场区机组尾流电量衰减比随之降低,缓冲带需达到一定的距离,对于风速的恢复有明显的作用,发电产能才能够有所提升;本案例不同场景下,缓冲带距离在23D～44D之间,尾流损失电量降幅在27%～4%之间。 结论  基于相邻风电场实际运行数据开展尾流分析可为后续海上大型风电基地规划设计和机组排布优化设计提供指导。     

考虑尾流效应及连接架构的风电场等值方法研究北大核心CSCD

对大型风电场进行完整的仿真分析会消耗巨大的时间成本。文章提出了一种基于尾流效应和连接架构的风电场等值法,考虑了风机位置对输入风速的影响,最大程度地保留了原有结构和性能,建立了简单准确的等值模型。分析了容量加权法的误差及原因,提出了根据风电场连接架构对风机群进行考虑尾流效应的分组方法,并通过容量加权法对分组进行参数聚合。通过Matlab/Simulink建立仿真模型,结果表明,所提模型的各项性能在不同风向下均能与原模型性能相近,具有较高的精确度和适应度。     

考虑尾流效应的风电场减载出力优化控制

在高风电渗透电力系统中,风电场参与系统调频普遍忽略了尾流效应对风电机组出力的影响。文章提出了一种考虑尾流效应的风电场减载出力优化控制方案。首先,采用改进Jensen尾流模型,给出了任意风向的尾流区域划分方法;其次,在满足系统调频需求的前提下,以风电场有功出力最大为目标,对风电场功率分配进行优化;然后,针对不同风速区间设置了相应的优化控制方案,并给出方案的具体实现方法;最后,以江苏某海上风电场为例进行算例仿真,算例结果表明,所提方案可有效地提高风电场整体出力。     

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。     

有功调整提升模式下风电场优化控制策略研究

针对风电场有功功率调整升功率模式下,考虑故障停机机组、降功率运行机组及低风速区机组对升功率贡献有限等情况,给出一种基于各机组出力能力差异分组的风电场有功调整提升控制策略和算法。同时,该算法将风电场运行环境温度过低、过高情况下期望运行机组的数量,以及避免机组频繁启停等因素作为风电场优化运行的约束条件。算法根据当前风力发电机组的运行状态、预测功率、故障代码等条件建立分组模型,结合风电场有功调整量及约束条件得出风电场机组最优运行的功率控制算法。仿真结果表明,算法能够保证风电场功率调整误差小,输出功率平稳,同时又避免机组的频繁启停对电网产生的冲击。     

考虑风速、风向变化及尾流效应的风电场建模

对风电场建模过程中,确立准确的输入风速模型至关重要。风电场中机组排列密集,风机之间存在尾流效应使得各台风机的输入风速不可能完全相同,尾流效应的强弱与很多因素有关,如:风速、风向、机组的排列布置等。利用MATLAB程序实现在不同风速、风向、风机排列情况下输入风速的计算,模拟实现对风速、风向变化情况下风电场的快速建模。可以较准确地描述出当风速、风向变化时风电场功率输出的变化。通过仿真验证了此方法的正确性,该方法的可移植性高,可用于各种规模风电场输入风速模型的计算。     

考虑尾流效应对风电场机组布局的影响分析

尾流效应的存在会导致风电场下风向风能减少,流场湍流度增加,进而影响风电场中位于下风向风机的效率和风轮的使用寿命。文章对尾流效应研究现状进行了概述,利用WASP软件以及风资源数据进行风电场模拟计算,将上下游风机之间间距以及上下游风机连线与主导风向的偏向角作为风机定位坐标,建立了分别由2台、3台、4台风机组成的模型并进行计算。比较在不同风机布局的情况下,风电场内每台风机和风电场的年净发电量以及尾流损失值随风机布局的变化趋势。对比计算结果得出风电场机组布局中风机之间的最佳间距和偏向角的定量值,确定风机尾流效应分析在风电场内机组布局中的重要性,为优化风电机组布局以及提高风电场风能利用率提供理论依据。     

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.     

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。     

An experimental investigation on the effect of individual turbine control on wind farm dynamics

Individual wind turbines in a wind farm typically operate to maximize their performance with no consideration of the impact of wake effects on downstream turbines. There is potential to increase power and reduce structural loads within a wind farm by properly coordinating the turbines. To effectively design and analyze coordinated wind turbine controllers requires control‐oriented turbine wake models of sufficient accuracy. This paper focuses on constructing such a model from experiments. The experiments were conducted to better understand the wake interaction and impact on voltage production in a three‐turbine array. The upstream turbine operating condition was modulated in time, and the dynamic impact on the downstream turbine was recorded through the voltage output time signal. The flow dynamics observed in the experiments were used to improve a static wake model often used in the literature for wind farm control. These experiments were performed in the atmospheric boundary layer wind tunnel at the Saint Anthony Falls Laboratory at the University of Minnesota using particle image velocimetry for flow field analysis and turbine voltage modulation to capture the physical evolution in addition to the dynamics of turbine wake interactions. Copyright © 2015 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.     

Identifying and characterizing the impact of turbine icing on wind farm power generation

Wind park power production in cold climate regions is significantly impacted by ice growth on turbine blades. This can lead to significant errors in power forecasts and in the estimation of expected power production during turbine siting. A modeling system is presented that uses a statistical modeling approach to estimate the power loss due to icing, using inputs from both a physical icing and a numerical weather prediction model. The physical icing model is that of Davis et al., 1 with updates to the simulation of ice ablation. A new approach for identifying periods of turbine blade icing from power observations was developed and used to calculate the observed power loss caused by icing. The observed icing power loss for 2years at six wind parks was used to validate the modeling system performance. Production estimates using the final production loss model reduce the root mean squared error when compared with the empirical wind park power curve (without icing influence) at five of the six wind parks while reducing the mean bias at all six wind parks. In addition to performing well when fit to each wind park, the production loss model was shown to improve the estimate of power when fit using all six wind parks, suggesting it may also be useful for wind parks where production data are not available. Copyright © 2015 John Wiley & Sons, Ltd.     

Using neural networks to estimate wind turbine power generation

This paper uses data collected at Central and South West Services Fort Davis wind farm (USA) to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach     

Optimal turbine spacing in fully developed wind farm boundary layers

As wind farms become larger, the asymptotic limit of the ‘fully developed’, or ‘infinite’, wind farm has been receiving an increased interest. This limit is relevant for wind farms on flat terrain whose length exceeds the height of the atmospheric boundary layer by over an order of magnitude. Recent computational studies based on large eddy simulation have identified various mean velocity equilibrium layers and have led to parameterizations of the effective roughness height that allow the prediction of the wind velocity at hub height as a function of parameters such as wind turbine spacing and loading factors. In the current paper, we employ this as a tool in making predictions of optimal wind turbine spacing as a function of these parameters, as well as in terms of the ratio of turbine costs to land surface costs. For realistic cost ratios, we find that the optimal average turbine spacing may be considerably higher than that conventionally used in current wind farm implementations. Copyright © 2011 John Wiley & Sons, Ltd.