基于网格-坐标化遗传算法的风电场布局优化

采用Jensen尾流模型来描述风电机组间尾流的干扰效应。首先基于网格化的改进遗传算法获得风电机组的数量和布局初始位置,再通过坐标化遗传算法对风电机组位置进行进一步调整优化,从而提高单位成本的发电量。根据所提出的方法,在3种不同的风场（定风向定风速、定风速变风向和变风速变风向）下,针对2 km×2 km的标准风场区域进行风电机组布局优化,再将其应用到不规则的实际案例,对比分析表明所提出的方法能有效提高发电量。     

Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation

As a result of the increasing wind power penetration on power systems, the wind farms are today required to participate actively in grid operation by an appropriate generation control. This paper presents a comparative study on the performance of three control strategies for DFIG wind turbines. The study focuses on the regulation of the active and reactive power to a set point ordered by the wind farm control system. Two of them (control systems 1 and 2) are based on existing strategies, whereas the third control system (control system 3) presents a novel control strategy, which is actually a variation of the control system 2. The control strategies are evaluated through simulations of DFIG wind turbines, under normal operating conditions, integrated in a wind farm with centralized control system controlling the wind farm generation at the connection point and computing the power reference for each wind turbine according to a proportional distribution of the available power. The three control systems present similar performance when they operate with power optimization and power limitation strategies. However, the control system 3 with down power regulation presents a better response with respect to the reactive power production, achieving a higher available reactive power as compared with the other two. This is a very important aspect to maintain an appropriate voltage control at the wind farm bus.     

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...     

The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms

Wind farm control using dynamic concepts is a research topic that is receiving an increasing amount of interest. The main concept of this approach is that dynamic variations of the wind turbine control settings lead to higher wake turbulence, and subsequently faster wake recovery due to increased mixing. As a result, downstream turbines experience higher wind speeds, thus increasing their energy capture. In dynamic induction control (DIC), the magnitude of the thrust force of an upstream turbine is varied. Although very effective, this approach also leads to increased power and thrust variations, negatively impacting energy quality and fatigue loading. In this paper, a novel approach for the dynamic control of wind turbines in a wind farm is proposed: using individual pitch control, the fixed‐frame tilt and yaw moments on the turbine are varied, thus dynamically manipulating the wake. This strategy is named the helix approach because the resulting wake has a helical shape. Large eddy simulations of a two‐turbine wind farm show that this approach leads to enhanced wake mixing with minimal power and thrust variations.     

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。     

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.     

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.     

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。     

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.     

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.     

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.     

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.     

Experimental verification of computational predictions in power generation variation with layout of offshore wind farms

The optimization of wind farms with respect to spatial layout is addressed experimentally. Wake effects within wind turbine farms are well known to be deleterious in terms of power generation and structural loading, which is corroborated in this study. Computational models are the predominant tools in the prediction of turbine‐induced flow fields. However, for wind farms comprising hundreds of turbines, reliability of the obtained numerical data becomes a growing concern with potentially costly consequences. This study pursues a systematic complementary theoretical, experimental and numerical study of variations in generated power with turbine layout of an 80 turbine large wind farm. Wake effects within offshore wind turbine arrays are emulated using porous discs mounted on a flat plate in a wind tunnel. The adopted approach to reproduce experimentally individual turbine wake characteristics is presented, and drag measurements are argued to correctly capture the variation in power generation with turbine layout. Experimental data are juxtaposed with power predictions using ANSYS WindModeller simulation suite. Although comparison with available wind farm power output data has been limited, it is demonstrated nonetheless that this approach has potential for the validation of numerical models of power loss due to wake effects or even to make a direct physical prediction. The approach has even indicated useful data for the improvement of the physics within numerical models. Copyright © 2014 John Wiley & Sons, Ltd.     

A passively self-adjusting floating wind farm layout to increase the annual energy production

Wake losses inside a wind farm occur due to the aerodynamic interactions when a downwind turbine is in the wake of upwind turbines. The ability of floating offshore wind turbines (FOWTs) to relocate their positions in the horizontal plane introduces an opportunity to decrease the wake losses in a floating wind farm (FWF). Our goal is to use this ability to passively move the downwind FOWT out of the wake of upwind ones. Since the mooring system (MS) attached to a FOWT is responsible for its station keeping, the horizontal motions of the FOWT depend on the MS design. Hence, if we can design the MS to passively move the FOWT out of the wake, we can increase the FWF annual energy production (AEP). In this paper, we investigate if we can benefit from relocating FOWTs in a FWF and increase its AEP. In addition, we present a novel approach that considers the ability of a FOWT to relocate its position as a new degree of freedom (DoF) in the FWF layout design. This means we will have a self-adjusting wind farm layout where the FOWTs passively re-arrange themselves depending on the wind direction and the wind speed. Consequently, we will have a slightly different wind farm layout for every wind direction and every wind speed. To achieve this layout, we include the MS design as part of the FWF's layout design. In a self-adjusting FWF layout, each FOWT is attached to a customized MS design allowing it to relocate its position in the best way possible according to the wind direction, to increase the overall AEP of the wind farm. The results of one case study show that the novel approach can increase the FWF's AEP by 1.6% when compared with a current state of the art optimized floating wind farm layout. Finally, we implemented our method as an open-source python tool to be used and enhanced further within the wind energy community.     

基于Park-Gauss模型考虑大气稳定性对布局优化的影响

基于Park模型尾流区线性膨胀假设和径向风速呈高斯分布假设,提出一种新的修正型的工程尾流模型Park-Gauss模型,采用小生境遗传算法,并考虑大气稳定性对风电场布局优化的影响。结果表明:对常风速单风向风电场微观选址布局优化结果是风力机组主要布置在垂直风向的第1排和最后1排;大气边界层稳定性对风电场微观选址布局优化影响显著,在大气边界层不稳定状态下,风电场安装机组总数最多、发电总量及风电场利用效率最高,中性状态和稳定状态依次次之。     

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.     

Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines

As a result of increasing wind farms penetration in power systems, the wind farms begin to influence power system, and thus the modelling of wind farms has become an interesting research topic. Nowadays, doubly fed induction generator based on wind turbine is the most widely used technology for wind farms due to its main advantages such as high-energy efficiency and controllability, and improved power quality. When the impact of a wind farm on power systems is studied, the behavior of the wind farm at the point common coupling to grid can be represented by an equivalent model derived from the aggregation of wind turbines into an equivalent wind turbine, instead of the complete model including the modelling of all the wind turbines. In this paper, a new equivalent model of wind farms with doubly fed induction generator wind turbines is proposed to represent the collective response of the wind farm by one single equivalent wind turbine, even although the aggregated wind turbines operate receiving different incoming winds. The effectiveness of the equivalent model to represent the collective response of the wind farm is demonstrated by comparing the simulation results of equivalent and complete models both during normal operation and grid disturbances.     

Model‐based receding horizon control of wind farms for secondary frequency regulation

In this study, we propose the use of model‐based receding horizon control to enable a wind farm to provide secondary frequency regulation for a power grid. The controller is built by first proposing a time‐varying one‐dimensional wake model, which is validated against large eddy simulations of a wind farm at startup. This wake model is then used as a plant model for a closed‐loop receding horizon controller that uses wind speed measurements at each turbine as feedback. The control method is tested in large eddy simulations with actuator disk wind turbine models representing an 84‐turbine wind farm that aims to track sample frequency regulation reference signals spanning 40 min time intervals. This type of control generally requires wind turbines to reduce their power set points or curtail wind power output (derate the power output) by the same amount as the maximum upward variation in power level required by the reference signal. However, our control approach provides good tracking performance in the test system considered with only a 4% derate for a regulation signal with an 8% maximum upward variation. This performance improvement has the potential to reduce the opportunity cost associated with lost revenue in the bulk power market that is typically associated with providing frequency regulation services. Copyright © 2017 John Wiley & Sons, Ltd.     

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。     

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.