湍流强度修正风力发电机组功率曲线方法的研究

依据现有IEC61400-12-1功率曲线测试标准,功率曲线是采用bin法得到的,未考虑湍流强度的影响。这就使得按照标准测试得到的功率曲线依赖于现场特定的湍流强度,具有不可移植性。同一机组在不同测试现场,测试得到的功率曲线都会不同,这会给测试功率曲线和担保功率曲线的对比带来很大难度。基于此,有必要考虑湍流强度对功率曲线的影响。     

大型风力发电机组调向功率的探讨

针对风力发电机组调向对风机构工作性能直接关系到机组的整体性能，从研究风力发电机组调向阻力矩入手，提出了确定机组调向功率需考虑的一些因素和方法。     

风力发电机组综合改造技术验证

近年来,风力发电非常迅速,然而很多已建风场因为风资源变差影响到了发电效果.为了能够获得更好的风力发电效果,文中,就某风场进行的国内首创塔筒增高和叶片加长综合改造科研验证进行了总结.通过验证结果,可知该方案可以用作已建风场风资源变差的补偿解决方案.     

基于运行数据的风力发电机组功率特性分析

针对实际1．5MW风电机组．通过获取反映机组运行性能的实测风速、功率等数据,采用Bin方法对数据进行处理后,获得风电机组的功率曲线,并将其推广到机组风能利用曲线的提取。通过计算得到了2台机组的实际运行功率曲线、风能利用曲线及其标准差值．对风电机组的运行性能进行了对比分析和评估。     

风电机组的功率曲线验证方法研究

功率曲线是风电机组发电能力的重要指标。本文讨论了功率曲线验证的必要性,并且提出了一种利用实测功率曲线与年发电量的相关性来考核厂家提供功率曲线是否达标的方法。以典型风电机组为例进行了研究分析。     

液压型风力发电机组最佳功率追踪控制研究

针对液压型风力发电机组能量转化效率问题,以最佳机组输出功率为控制目标,提出了两种最佳功率追踪方法。以30 k VA液压型风力发电机组实验平台为基础,研究所提出控制方法的可行性与控制特点。仿真和实验结果表明:直接压力控制的最佳功率追踪控制方法简单易行,但其准确性易受系统效率影响;考虑系统效率时,系统压力和风力机转速联合控制的最佳功率追踪控制方法具有更精确的追踪效果。     

风能资源评估中的功率曲线问题

本文通过利用不同地区的3座测风塔的完整年测风数据进行计算,比较了利用静态功率曲线和动态功率曲线计算得到的年发电量的不同,分析了其中的规律。通过比较不同条件下利用静态功率曲线与动态功率曲线计算结果的差异得出,不同条件下,动态功率曲线计算结果相对静态功率曲线计算结果百分比的经验值,对以后直接利用静态功率曲线计算年发电量具有较好的指导作用。     

全功率变频风力发电机组惯性响应控制

针对频率波动时全功率变频风电机组不能对电网提供频率支撑的问题,文章将虚拟惯性调节和下垂调节相结合,利用全功率变频风电机组的惯性控制技术参与电网调频,将风机有功出力设定在低于最大功率追踪,使其有足够空间进行有功调节,以及惯性环节结束后发电机转子转速快速恢复。仿真结果证明了文章控制策略的有效性。     

无刷双馈风力发电机组的自抗扰功率解耦控制

对无刷双馈风力发电机组稳态运行时的功率分配关系进行了详细分析,在此基础上确定了最大风能捕获的控制策略.将自抗扰控制应用到无刷双馈电机有功功率与无功功率的解耦控制,将功率控制系统分解为有功功率子系统和无功功率子系统,从而建立了风力发电机组完整的功率控制模型.基于Matlab/Simulink的仿真结果表明无刷双馈风力发电机组自抗扰控制成功实现了有功功率与无功功率的解耦控制,不仅能够实现最大风能捕获,而且可以根据电网的实际需求调节机组无功功率的输出,验证了控制算法的有效性.     

基于修改功率跟踪特性曲线的风电机组减载调频策略研究

针对现有风电机组调频策略中增益系数难以选取的问题,提出一种新颖的风电机组减载调频策略,创新性地取消了辅助频率控制,通过修改功率跟踪特性曲线来提供惯性和一次调频响应.该策略可保证调频过程中任意风速下,机组都可收敛到平衡点,避免因能量过度释放而引起欠速停机问题.该策略在调频控制环节中不含有增益系数,适用于不同风速下的风电机...     

基于风电机组功率曲线的故障监测方法研究

为了最大限度地减少风机停机时间和提高风机发电量,基于风机功率曲线特性,结合多元统计Hotelling T~2控制图,提出了一种风力发电机性能及故障监测方法。首先,根据SCADA系统历史数据集,应用粒子群算法(PSO)寻优最小二乘支持向量机的模型,构造风电机组参考功率曲线。然后,计算风场各风机功率特性的多元峰度、多元偏度,将其偏离参考曲线的程度作为评估风力发电机性能的指标。最后,监测风机发生故障的时刻,引入用于监测风机的Hotelling T~2多变量质量控制图。将该方法用于某风场1.5 MW级风力发电机,实例表明,该算法可以有效地对风电机组状态及故障进行监测,为风电机组的故障识别及分析提供了一种新的方法。     

Ice detection on wind turbines using the observed power curve

Icing on the blades of a wind turbine can lead to significant production losses during the winter months for wind parks in cold climate regions. However, there is no standard way of identifying ice‐induced power loss. This paper describes three methods for creating power threshold curves that can be used to separate iced production periods from non‐iced production periods. The first approach relies on a percentage deviation from the manufacturer's power curve. The other two approaches fit threshold curves based on the observed variance of non‐iced production data. These approaches are applied to turbines in four wind parks and compared with each other and to observations of icing on the nacelle of one of the turbines in each park. It is found that setting an ice threshold curve using 0.1 quantile of the observed power data during normal operation with a 2‐h minimum duration is the best approach for icing identification. The quantile should be fit based on at least 1year of data, and a smoothing function should be applied to the quantile results to remove any outliers caused by limited numbers of data points. Copyright © 2015 John Wiley & Sons, Ltd.     

Counter-rotating type pump-turbine unit cooperating with wind power unit

This serial research proposes the hybrid power system combined the wind power unit with the counter-rotating type pump-turbine unit,to provide the constant output for the grid system,even at the suddenly fluctuating/turbulent wind.In this paper,the tandem impellers of the counter-rotating type pumping unit was operated at the turbine mode,and the performances and the flow conditions were investigated numerically and experimentally.The 3-D turbulent flows in the runners were simulated at the steady state condition by using the commercial CFD code of ANSYS-CFX ver.12 with the SST turbulence model.While providing the pump unit for the turbine mode,the maximum hydraulic efficiency is close to one of the counter-rotating type hydroelectric unit designed exclu-sively for the turbine mode.Besides,the runner/impeller of the unit works evidently so as to coincide the angularmomentum change through the front runners/impellers with that through the rear runners/impellers,namely to take the axial flow at not only the inlet but also the outlet without the guide vanes.These results show that this type of unit is effective to work at not only the pumping but also the turbine modes.     

Acoustic noise from tandem wind rotors of intelligent wind power unit

The authors had invented the unique wind power unit composed of the large-sized front wind rotor, the small-sized rear wind rotor and the peculiar generator with the inner and the outer rotational armatures without the conventional stator. This unit is called ＂Intelligent Wind Power Unit＂ by the authors. The front and the rear wind rotors drive the inner and the outer armatures, respectively, while the rotational torque is counter-balanced between both armatures/wind rotors. This paper discusses experimentally the acoustic noise from the front and the rear wind rotors. The acoustic noise, in the counter-rotating operation, is induced mainly from the flow interaction between both rotors, and has the dominant power spectrum density at the frequency of the blade passing interaction. The noise is caused mainly from the turbulent fluctuation due to the flow separation on the blade, when the rear wind rotor stops or rotates in the same direction as the front wind rotor.     

大型风电机组功率曲线的分析与修正

讨论了风电机组不同情况下的功率曲线定义,分析了功率曲线绘制过程中的风速处理方法,可以适用于绘制风力发电机组静、动态功率曲线;讨论了影响机组功率曲线的各种因素,并给出了影响因子,使得根据功率曲线进行风场发电量的计算可以取得更可靠的结果。     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post‐process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non‐linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non‐linear non‐parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non‐linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non‐parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power‐to‐wind) transformation, simpler linear regression models with censoring perform equally or better than non‐linear models with or without the frequently used wind‐to‐power transformation. © 2013 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

Quick method of obtaining the power curve of a wind energy conversion system

This study proposes a method which makes it possible to reduce the number of tests needed to obtain the power curve of a Wind Energy Conversion System (WECS) by extracting more useful information from a test. When using the method of bins, recommended by the International Energy Agency, to determine the power curve, the pre-average occults the wind's irregularity. The study of this irregularity allows to give a weighting to each test data. Before defining this method two lines of study were developed.

The first one concerns the regularity of a function and its application to the variation, for a given machine, of the wind vector module over time. The second one deals with the estimation of the mean value of a random variable by the calculation of the mean of the observed values, given a weighting by a coefficient allowing the diminution or increase then importance of a result.

For a given place, whose turbulence intensity during the measurements is known, the weighted curve and the unweighted curve should be the same. Using the weighting brings either increase in the accuracy or decrease in the number of tests. This method has been tested on two WECS. The tests were carried out at the Centre National d'Essais Eoliens de Lannion (CNEEL France). Then the results are set out and commented.     

Redefinition power curve for more accurate performance assessment of wind farms

The lack of accurate methods for assessment of the productive capacity of wind power plants is becoming a bottleneck in an increasingly commercialized wind power industry. In this article the inherent components of performance assessment are identified and analysed and ways of minimizing uncertainties on the components are investigated. The main components are identified as ‘site calibration’, ‘wind turbine sensitivity to flow variables’, ‘plant blockage effects’ and ‘uncertainty analysis’. Site calibration is the action of estimating the flow variables at the wind turbine position from measurements of these quantities at another (reference) position. The purpose of sensitivity analysis is to clarify which and how flow variables influence power output. Plant blockage effects refer to the power plant's influence on the reference measurements of flow variables. Finally, the component uncertainties and in turn the integrated uncertainty on the average productive capacity of the wind power plant are investigated. It is found that uncertainties can be reduced (1) by including several more flow variables in addition to hub‐height wind speed, (2) by carrying out site calibration with utmost care and by inclusion of more variables, (3) by taking plant blockage into consideration, (4) by aiming at ‘plant‐average’ power instead of looking only at individual machines and, possibly, (5) by introduction of remote‐sensing anemometer techniques. Copyright © 2000 John Wiley & Sons, Ltd.