A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.     

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.     

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.     

Large eddy simulations of the effect of vertical staggering in large wind farms

In order to study the effect of vertical staggering in large wind farms, large eddy simulations (LES) of large wind farms with a regular turbine layout aligned with the given wind direction were conducted. In the simulations, we varied the hub heights of consecutive downstream rows to create vertically staggered wind farms. We analysed the effect of streamwise and spanwise turbine spacing, the wind farm layout, the turbine rotor diameter, and hub height difference between consecutive downstream turbine rows on the average power output. We find that vertical staggering significantly increases the power production in the entrance region of large wind farms and is more effective when the streamwise turbine spacing and turbine diameter are smaller. Surprisingly, vertical staggering does not significantly improve the power production in the fully developed regime of the wind farm. The reason is that the downward vertical kinetic energy flux, which brings high velocity fluid from above the wind farm towards the hub height plane, does not increase due to vertical staggering. Thus, the shorter wind turbines are effectively sheltered from the atmospheric flow above the wind farm that supplies the energy, which limits the benefit of vertical staggering. In some cases, a vertically staggered wind farm even produced less power than the corresponding non vertically staggered reference wind farm. In such cases, the production of shorter turbines is significantly negatively impacted while the production of the taller turbine is only increased marginally.     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

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。     

基于风电机组状态的超短期海上风电功率预测

提出一种基于风电机组状态的超短期海上风电功率预测模型。首先,综合考虑海上环境因素以及风电机组部件间的相互作用建立指标的预测模型,以长短期记忆神经网络的预测误差作为监测指标的动态劣化度;然后采用模糊综合评价法对风电机组的运行状态进行评估,依据评估结果对风电机组历史运行数据进行划分;最后根据分类后历史运行数据建立基于机组状态的超短期风电功率预测模型。结合国内某海上风电场实例数据进行分析,算例结果表明所提方法可有效提高风电功率预测精度。     

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.     

Power output variations of co-located offshore wind turbines and wave energy converters in California

The electric power generation of co-located offshore wind turbines and wave energy converters along the California coast is investigated. Meteorological wind and wave data from the National Buoy Data Center were used to estimate the hourly power output from offshore wind turbines and wave energy converters at the sites of the buoys. The data set from 12 buoys consists of over 1,000,000 h of simultaneous hourly mean wind and wave measurements. At the buoys, offshore wind farms would have capacity factors ranging from 30% to 50%, and wave farms would have capacity factors ranging from 22% to 29%. An analysis of the power output indicates that co-located offshore wind and wave energy farms generate less variable power output than a wind or wave farm operating alone. The reduction in variability results from the low temporal correlation of the resources and occurs on all time scales. Aggregate power from a co-located wind and wave farm achieves reductions in variability equivalent to aggregating power from two offshore wind farms approximately 500 km apart or two wave farms approximately 800 km apart. Combined wind and wave farms in California would have less than 100 h of no power output per year, compared to over 1000 h for offshore wind or over 200 h for wave farms alone. Ten offshore farms of wind, wave, or both modeled in the California power system would have capacity factors during the summer ranging from 21% (all wave) to 36% (all wind) with combined wind and wave farms between 21% and 36%. The capacity credits for these farms range from 16% to 24% with some combined wind and wave farms achieving capacity credits equal to or greater than a 100% wind farm because of their reduction in power output variability.     

Blockage effects in wind farms

An experimental study of wind farm blockage has been performed to quantify the velocity decrease that the first row of a wind farm experiences due to the presence of the other turbines downstream. The general perception has been that turbines downstream of the first row are only influenced by the wakes from upstream turbines without any upstream effect. In the present study, an attempt is made to demonstrate the existence of a two‐way coupling between individual turbines and turbines in the wind farm. Several staggered layouts were tested in the wind tunnel experiments by changing the spacing between rows, spacing between turbines in the rows, and the amount of wind turbines involved. The experiments focused on turbines located in the center of the first row as well as the two turbines located in the row edges, usually believed to experience a speedup. The present results show that no speedup is present and that all the turbines in the first row are subjected to a reduced wind speed. This phenomenon has been considered to be due to “global blockage.” An empirical correlation formula between spacing, number of rows, and velocity decrease is proposed to quantify such effect for the center turbine as well as for the turbines at the edges.     

海上风电场智慧运维管理系统

  目的  针对海上风电场运维安全管理,提出了海上风电场智慧运维管理系统。  方法  通过海上风电智慧调度系统、海上风电雷达多源跟踪及边界警示系统、海上风电场风机平台作业监管系统,搭建出海上风电场智慧运维管理系统。  结果  通过陆上集控中心的海上风电智慧调度系统,实现人员的安全管理以及船舶调度。通过海上风电雷达多源跟踪及边界警示系统,实现海域船只全范围跟踪,并确保海上风电场的风机及海缆安全。通过风机平台的海上风电场风机平台作业监管系统,实现风机作业人员在风机平台的全面管理。  结论  研究的系统实现了海上风电场人、船、风机的全面管理,有力保障了人员的安全管理以及船舶调度,提高了海上风电场的风机及海缆安全性,实现海上风电场智慧运维效率,有望在工程中应用推广。     

Windstorm risk assessment for offshore wind farms in the North Sea

The North Sea is becoming increasingly attractive to wind energy developers and investors, with 38 wind farms belonging to five different countries and representing over€35 billion of assets. Concerns about offshore wind turbines being damaged by extreme windstorms pose a challenge to insurers, investors and regulators. Catastrophe modeling can adequately quantify the risk. In this study, a Monte Carlo simulation approach is used to assess the number of turbines that buckle using maximum wind speeds reaching each wind farm. Damage assessment is undertaken for each wind farm using a log‐logistic damage function and a left‐truncated Weibull distribution. The risk to offshore wind power in the North Sea is calculated using an exceedance probability (EP) curve for the portfolio of wind farms. The European Union Solvency II directive requires insurance companies to hold sufficient capital to guard against insolvency. The solvency capital requirement (SCR) is based on a value‐at‐risk measure calibrated to a 99.5% confidence level over a 1‐year time horizon. The SCR is estimated at €0.049 billion in the case of yawing turbines. Simulations are repeated for different climate change scenarios. If wind speeds grow by 5% and the frequency of storms increases by 40%, the SCR is seen to rise substantially to €0.264 billion. Relative to the total value of assets, the SCR is 0.14% compared with 0.08% for European property, confirming that these wind farm assets represent a relatively high risk. Furthermore, climate change could increase the relative SCR to levels as high as 0.75%.     

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.     

A simple analytical wind park model considering atmospheric stability

The analytical top‐down wind park model by Emeis and Frandsen 1 is enhanced by consistently making both the downward momentum flux and the momentum loss at the rough surface dependent on atmospheric stability. Specifying the surface roughness underneath the turbines in a wind farm in the model gives the opportunity to investigate principal differences between onshore and offshore wind parks, because the roughness length of the sea surface is two to three orders of magnitude lower than the roughness length of land surfaces. Implications for the necessary distance between single turbines in offshore wind farms and the distance between neighbouring wind parks are computed. It turns out from the model simulations that over smooth surfaces offshore the wind speed reduction at hub height in a wind farm is larger than over rough onshore surfaces given the same density of turbines within the park. Mean wind profiles within the park are also calculated from this model. Offshore wind farms must have a larger distance between each other in order to avoid shadowing effects of the upstream farm. Copyright © 2009 John Wiley & Sons, Ltd.     

Availability,operation and maintenance costs of offshore wind turbines with different drive train configurations

Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with two‐stage and three‐stage gearboxes. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

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。     

Benefits of collocating vertical‐axis and horizontal‐axis wind turbines in large wind farms

In this study, we address the benefits of a vertically staggered (VS) wind farm, in which vertical‐axis and horizontal‐axis wind turbines are collocated in a large wind farm. The case study consists of 20 small vertical‐axis turbines added around each large horizontal‐axis turbine. Large‐eddy simulation is used to compare power extraction and flow properties of the VS wind farm versus a traditional wind farm with only large turbines. The VS wind farm produces up to 32% more power than the traditional one, and the power extracted by the large turbines alone is increased by 10%, caused by faster wake recovery from enhanced turbulence due to the presence of the small turbines. A theoretical analysis based on a top‐down model is performed and compared with the large‐eddy simulation. The analysis suggests a nonlinear increase of total power extraction with increase of the loading of smaller turbines, with weak sensitivity to various parameters, such as size, and type aspect ratio, and thrust coefficient of the vertical‐axis turbines. We conclude that vertical staggering can be an effective way to increase energy production in existing wind farms. Copyright © 2016 John Wiley & Sons, Ltd.     

Feasibility of large scale offshore wind power for Hong Kong — a preliminary study

This paper investigates the potential and the feasibility of offshore wind energy for Hong Kong. The 1998 wind data taken from an island were analysed. The wind resource yields an annual mean wind speed of 6.6 m/s and mean wind power density of 310 W/m². With commercially available 1.65 MW wind turbines placed on the whole of Hong Kong’s territorial waters, the maximum electricity generating potential from offshore wind is estimated to be 25 TWh which is about 72% of the total 1998 annual electricity consumption. However, potential is significantly reduced if other usages of the sea such as shipping are considered. A hypothetical offshore wind farm of 1038 MW capacity is then sited on the East-side waters. The extreme wind and wave climates, as well as the seasonal variation of wind power and demand are examined. The electricity generation costs are estimated and compared with the local retail tariff. Initial results indicate the wind farm is economically viable and technically feasible.