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。     

主动偏航尾流控制研究成果综述

基于偏航尾流模型、偏航尾流特性及主动偏航尾流控制(Active Yaw Control, AYC)对风场的影响三个角度,调研了主动偏航控制技术最近几年来的研究进展,并针对目前研究的一些局限性以及AYC的工程应用实践性作出展望。调研结果显示：偏航会改变风机的尾流特性,主要表现在尾流的风速分布和偏航尾流偏置量上;整体来看上游风机主动偏航能提升整场发电量,但同时也会改变风机的载荷;目前国内外常用的几个偏航尾流模型中,Bastankhah模型和Qian-Ishihara模型与实测数据的匹配性较好,具有很高的工程实践性。同时,在后续偏航尾流研究中,应基于风场实际数据对偏航尾流特性和模型进行验证与分析。     

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...     

基于SCADA数据海上风电场解析尾流模型可靠性与尾流叠加研究北大核心CSCD

针对工程中常用的解析尾流模型适用性及精确度存在较大不确定性的问题,文章利用国内某海上风电场运行的SCADA数据,分别对Jensen模型、Frandsen模型和Gaussian模型等主要解析尾流模型进行可靠性验证和分析。在此基础上,提出了更加适用的尾流模型参数选取方案,参数修正后的Gaussian尾流模型的评估精度提升约50%。此外,为了分析海上风电场的尾流叠加特性,文章采用场内实测数据进行分析,并与工程尾流叠加模型计算结果进行对比,结果表明,工程尾流叠加模型仍存在一定改善空间。     

平坦地区风电场间尾流影响的计算分析

风资源的分布具有区域性,大中型风场在这些区域往往就会集中成片分布,风场间的相互影响就不可避免地出现。通过在学习风场的周围,假定参考风场8扇区均匀分布的方式,着重研究了距离和分布方位对于风场尾流值的影响,通过分析对比得出以下结论:1)随着距离增加,参考风场对学习风场尾流值影响越小,随着距离的增加,尾流值相对误差的衰减逐步减弱;2)参考风场分布方位对于学习风场影响较大,沿着主导风向的上风向分布的风场对于学习风场影响较大,与非主导风向分布的风场相比,单一风场分布时,比值在10倍左右,成片风场分布时,比值在5倍左右;3)距离相同时,成片分布风场的影响较单一分布的风场要大,平均的尾流相对误差是其3倍左右。     

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。     

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。     

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。     

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。     

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.     

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.     

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.     

Survey of wind farm control—power and fatigue optimization

The main goal of this paper is to establish the present state of the art for wind farm control. The control area that will be focused on is the mechanical/aerodynamic part, which includes the wind turbines, their power production, fatigue and wakes affecting neighbouring wind turbines. The sub‐objectives in this area of research are as follows: (i) maximizing the total wind farm power production; (ii) following a reference for the total wind farm active power; and (iii) doing this in a manner that minimizes fatigue loading for the wind turbines in the farm. Each of these sub‐objectives is discussed, including the following important control issues: choice of input and output, control method and modelling used for controller design and simulation. The available literature from industry is also considered. Finally, a conclusion is presented discussing the established results, open challenges and necessary research. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.     

Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups

Several different models provided by researchers to maintain a wind turbine, but most of these models only focused on the case involved a single objective optimization problem. In practice, real cases of wind farms lead to multi-objective approach to optimize maintenance efforts. In this paper, based on an opportunistic approach, a multi-objective based model is proposed to optimize the maintenance of a farm involved several different types of wind turbines. The assumptions of stochastic behavior of wind velocity as well as the existence of a limited number for maintenance groups are also considered in this new approach. The proposed model considering imperfect maintenance, attempts (1) maximizing the expected rate of energy and (2) minimizing the total expected costs related to maintenance efforts. The opportunistic approach is also provided by the component's reliability threshold values. The comparative analysis addresses that the capability of the proposed model is more efficient compared to models addressed in literature.     

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.     

考虑集电网络的风电场建模研究

为提高风电场模型的精度,文章提出建立一种考虑到集电网络影响的模型方法,该模型采用猴群算法计算等值参数。在此模型的基础上对风电场进一步化简,采用基于扩散距离的谱聚类算法对风电机组进行动态分群,得到两机表征的风电场模型,并进行仿真建模过程。通过对一个12 MW的详细风电场模型的仿真分析,将几种模型分别进行了对比,实验结果表明,文章提出的方法是准确有效的,所得到的两机表征模型能很好的反映出风电场并网的动态特性。     

Numerical simulations of fatigue loads on wind turbines operating in wakes

Wake effects increase the fatigue loads on wind turbines in operation. However, the wake flow is considerably different from the traditional boundary layer flow, and poses many challenges in determining the fatigue loads on wind turbines operating in a wake. Therefore, in the present study, the actuator‐line model was adopted to numerically simulate the wake flow and an in‐house code named AOWT, which is based on a generalized coordinate method, was developed for analyzing the dynamics of wind turbines under an arbitrary distribution of the turbulent flow field varying in time and space. Using the numerically modeled instantaneous wake flow fields and AOWT, the dynamic response of a wind turbine, located at specified positions in both tandem and staggered arrangements in a wake, was examined, and the fatigue loads were determined. Furthermore, to determine the major contributions to the fatigue loads, the loads induced by the spatial variation of the mean flow fields were predicted. To the best of the authors' knowledge, no such analysis has been conducted thus far. Importantly, it was found that in the near‐wake region, the mean flow field had a significant influence on the fatigue loads, especially in the staggered layout. However, there is no analytical wake model available in the literature capable of predicting the near‐wake mean flow fields. Therefore, in this study, a near‐wake model was proposed, which yielded satisfactory predictions of the mean velocities in the near‐wake region.