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。     

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。     

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。     

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。     

基于综合经济效益目标的风电机组偏航控制策略研究

基于偏航系统发电量仿真计算和偏航轴承疲劳寿命量化分析,建立了一种偏航系统重启对风控制模型。该模型以风电场寿命周期综合经济效益为目标,以控制偏航系统启停为策略,能够合理地平衡发电量与偏航次数之间的关系。为寻求最优控制策略,采用粒子群-遗传混合优化算法(PSO-GA)对该控制模型进行优化。结果表明:该偏航系统重启对风控制模型可以达到预期的优化目标,对风电场综合经济效益的提高具有指导意义。     

基于改进二进制萤火虫算法的风电场微观选址优化研究

风电场微观选址时应合理布置机组位置,减少尾流影响,提高风电场的综合经济效益。首先,针对不同布机方案及不同来流风向、风速下风电场尾流分布的变化,提出了一种基于坐标变换的风电场尾流场快速计算方法以提高计算效率;然后,对传统二进制萤火虫算法进行改进,从而提高寻求全局最优的能力;最后,基于以上工作,以机组总台数和布机方案为决策变量,以单位功率成本为目标函数,完成了3种风况条件下的风电场微观选址优化工作。经过计算对比分析,本方法在3种风况条件下均得到了更优的布机结果,对工程中海上风电场和陆上平坦地形风电场的微观选址工作具有参考价值。     

限电情况下风电场内机组最优组合方案研究

近年来,由于风电渗透率持续增加而电网消纳能力有限,造成风机产能过剩,"弃风"现象开始凸显。为保证电力系统的稳定运行,要求风电场主动参与系统调频,根据电网调度部门指令控制其功率输出。将风速预测信息与尾流效应模型结合起来,预测场内每台机组的最大出力。在此基础上,以提高风电场效益为目标,建立了限电情况下风电场内机组组合问题的数学模型,并利用改进的粒子群算法进行优化研究,得到限电情况下风电场内机组最优组合方案。最后通过实际算例进行仿真分析,验证了所提出方法的可行性和优越性。     

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。     

出力受限风电场群有功分配多目标优化策略

受风电发展与电网建设、负荷分布不协调等因素影响,弃风限电问题日益严峻。文章提出一种适用于因限风导致出力受限的风电场群有功分配多目标优化策略,该策略采用二次移动平均法对风电功率进行超短期预测,以风电场群实际有功出力与出力限值的差额最小以及输送线路损耗最小为目标,同时考虑风电场输出功率约束、低预测功率约束、避免机组频繁启停约束条件,基于改进遗传算法对各风电场群分配有功出力任务。以东北某实际风电场群为例,对所提出策略的可行性进行了验证。     

Fatigue distribution optimization for offshore wind farms using intelligent agent control

A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large‐scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re‐dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large‐scale wind farms. Copyright © 2012 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.     

海上风电场运行期尾流损失分析

  [目的]  为了充分认识海上风电场运行过程中的尾流效应,对风电场布局设计中的模拟计算结果进行验证,探索海上风电场的风机尾流损失变化规律。  [方法]  以华南地区某海上风电场为测试场址,选用PARK模型进行尾流模拟计算,对模型中的参数进行优化并进行实际发电量验证。  [结果]  结果表明:PARK模型用于海上风电场尾流模拟可以基本反映风机实际发电情况;在某风向上风机间距为7D情况下,主风向尾流损失在第2排后的分布规律呈现较为稳定的状态,约为首台风机的30%。  [结论]  PARK尾流模型能够较好的模拟近海风电场尾流损失和进行发电量计算,模型参数选择应根据项目实际情况进行敏感性测算。     

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.     

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.     

Probabilistic capacity of a grid connected wind farm based on optimization method

This paper proposes a new method to find the capacity of a future wind farm regarding several limits of the grid system: voltage stability limits, thermal limits, voltage limits, load tap changing limitation and generator power output limitation. The method combines the optimization method and a probabilistic analysis to maximize the number of the wind turbines subject to those limits. Two types of wind farms are modeled in this paper: fixed speed wind farms and variable speed wind farms. It is concluded that the proposed method is a fast and accurate method to deal with the uncertainty of wind energy in the planning of wind farm capacity.     

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.     

水平轴风力机组风场全局优化策略研究

为优化风场布置,减小上游风力机尾迹影响,以实现风场全局优化,基于致动线方法,利用OpenFOAM(多物理场运行与操作开源软件)对风力机组风场进行了15种风轮俯仰工况及9种错排布置的数值模拟,比较各优化策略下的风场总输出功率,并结合流场细微结构参数分布,分析不同优化方法对风场全局影响的流动机理。结果表明:尾迹对风场下游风力机影响严重。两种数值模拟优化方法均可实现风场全局优化,其中风轮俯仰优化策略可使风场总输出功率最大提高34.5%;风力机组错排布置可提高68.5%。此外,风场上游风力机功率在风轮俯仰时下降明显,风力机组错排时几乎无变化。