基于风电场等效平均风速的风电功率日前预测研究

提出风电机组风速模型及风电场等效平均风速模型,并由此得到基于风电场等效平均风速的风电功率预测方法,与以往基于平均风速的预测方法进行工程应用效果对比,该方法较基于平均风速的预测方法在预测精度方面有明显提高,且模型简易可行,具有通用性。     

基于MMAS BP算法的短期风速非线性组合预测模型

为提高风电场短期风速的预测精度,引入一种基于改进蚁群算法优化神经网络的非线性组合预测方法,按误差平方和最小原则对所建灰色GM(1,1)模型、BP网络和RBF网络三种单一预测数据进行非线性组合,并将其结果作为最终预测值。仿真结果表明,该方法的平均绝对误差及均方误差分别为17.76%和3.68%,均小于单一模型、线性组合模型及神经网络组合模型的预测结果,提高了网络的泛化能力,降低了预测风险,为风电场风速预测提供了一种新途径。     

基于混合Copula优化算法的风速预测方法研究

针对风电场中各风电机组风速之间存在的复杂时空相关性问题,提出一种基于混合Copula优化算法的风电场风速预测方法。该方法首先分析单一Copula函数拟合优度检验,选取合适Copula函数进行组合;其次,构建混合Copula函数模型对风电场内多风电机组风速相关性进行分析;最后应用最大期望（EM）算法求解模型相关系数并完成风速预测。结合优化算法,改进Copula函数能很好地解决风速相关性问题,为获取准确风速预测值奠定基础。以中国某地区风电场风电机组实测风速数据为例对所提方法进行验证,实验结果表明该模型可在准确分析风速相关性的基础上提高风速预测准确性。     

基于卡尔曼滤波修正的多步风电功率预测

针对传统时间序列预测多步风速时不能预测突变风速使风电功率预测误差较大的问题,采用基于数值天气预报(numerical weather prediction,NWP)风速及历史风速修正的卡尔曼滤波法对NWP风速进行多步修正,并通过修正后的NWP风速进行多步功率预测,第16步风速平均绝对误差降低了0.47 m/s,将该修正NWP风速与支持向量回归相结合,构建风电功率预测模型。构建模型与ARIMA模型及NWP直接预测模型相比,误差分别降低了6.8%和8.4%。应用该模型对山东某地区风电场现场数据进行仿真测试,第16步预测准确率达到82.6%。     

基于风速变化周期的风电场功率分类组合预测模型

基于风速在日时间尺度下的变化周期,提出一种风电场功率分类组合预测模型。该模型采用Morlet小波变换,分析数值天气预报中的风速在日时间尺度下的变化周期及特征;结合主成分分析和谱聚类方法对具有不同周期特征的风速变化过程进行分类;针对不同的风速变化类型分别建立遗传优化BP神经网络、RBF神经网络和支持向量机的预测模型,并选取每类对应的最优算法进行组合,预测功率时根据未来风速过程动态切换相应模型。以中国某风电场为例进行验证,结果表明,按8 h的变化周期对风速变化类型进行分类,可得到较好的分类组合预测结果,其精度较单一预测模型提高0.87%,合格率提高1.05%,验证了所提模型的有效性,为风电场功率预测提供了新思路。     

基于FCM-等值风速模型的风功率日前预测

由于风速的随机性、间歇性,以及风电场内各机组风速、功率的分散性,给风功率预测带来了较大难度。在计算风速线性相关的权值基础上,提出了改进模糊C均值聚类算法（fuzzy c-means,FCM）的风速模型,建立了风电场等值风速与改进FCM风速的关系函数。以某风电场实测数据进行验证,结果表明：所提风电功率预测方法算法简单;该方法预测精度提高了71.35%。在该风电场不同日周期下,验证了所提预测方法的有效性和普适性。     

基于粗糙集理论的遗传神经网络风速预测模型

风速预测对风电场和电力系统的运行具有重要意义。为了提高风速预测的精度,提出了一种新的风速预测方法——基于粗糙集理论的遗传神经网络模型。由于影响风速预测的因素很多,利用粗糙集理论的属性约简对神经网络输入的影响因素进行约简,识别出与预测风速相关性较大的影响因素作为输入量,减少了神经网络的计算量;利用全局搜索能力强的遗传算法对神经网络的初始权值进行优化,克服了神经网络收敛速度慢和容易陷入局部极小的缺点。实例结果表明该算法能够有效地提高预测的速度与精度,证明了该方法在风速预测中的可行性和有效性。     

基于非线性预测控制的风电场功率提升控制

随着大型风电场的快速发展,减小尾流效应造成的风电场能量损失成为研究热点之一。针对风电场实际运行中的风速变化以及尾流延时问题,基于尾流延时模型,建立考虑时间变量的风电场功率预测模型。以风电场输出功率最大化为目标,设计了非线性预测控制器,该控制器采用非线性预测模型,并采用PSO算法对预测时域内的性能指标进行优化,得到各台风机的控制值。基于Sim Wind Farm软件对该控制策略进行验证,并与传统风电场控制策略进行仿真比较,结果表明,这种新控制策略可以有效提升风电场的总体功率。     

不同特征向量下基于SVM的短期风速预测

选取广东省某风电场的测风数据,运用支持向量机(SVM)的方法对其进行短期风速预测。为提高预测的精度,通过LIBSVM回归机的交叉验证函数确定最优参数,建立4种不同输入特征向量组合(风速序列、风速和风向、风速和气压、风速风向和气压)的模型,分别预测该风场的短期风速,并对4种模型的预测误差进行分析和比较。实验结果表明:气压不宜作为输入特征向量;选用风速和风向作为输入特征向量的模型,预测效果最理想,其平均绝对百分比误差为12.8%。     

计及随机波动风速、风向的风电场建模方法研究

提出风电场建模方法建立风电场随机模型,对风电场运行特性进行仿真,为实现大规模风力发电的可预测、可控制目标服务。通过对风电场等值模型与详细模型的仿真比较,验证建模方法的合理性,并得出在研究风电场动态特性及其对电网影响时应考虑风速、风向的随机波动建立风电场模型。     

风电场风电机组的接地设计

较系统地介绍了风电场风电机组对接地电阻的要求、接地设计思路及方法,并提供实际工程中接地网布置图实例作为参考。     

风 风能 风力发电——21世纪新型清洁能源

一风的一般属性１风的形成风是人们非常熟悉的一种自然现象，人人都能感觉到它的存在。春风和煦，给万物带来生机；夏风吹拂，使人心旷神怡；秋风送爽，带来丰收的喜悦；冬风呼啸，迎来漫天飞雪。那么风是怎样形成的呢？众所周知，人类生活的地球表面被大气所包围，来自太阳的辐射不断传送到地球表面，因太阳辐射受热情况不同，地球表面各处的气温不同。在影响气压高低的因素中，气温起着最重要的作用。温度高的地区空气受热上升，气压减小；温度低的地方，空气下降，气压增大，于是产生了气压差。和水往低处流一样，空气也从气压高处向气压…     

Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.     

分散式风力发电机组的电压控制

风电的分散式开发不同于大规模开发和分布式开发,由于分散风电靠近负荷中心,直接接入配电网,且不加装无功补偿调节装置SVC,配网中较大的电压波动给分散风电的并网运行带来影响。文章讨论了配网对分散风电的电压控制特点和要求,结合风电机组无功控制能力,并推导出满足配网电压调节要求的风电机组无功控制范围和对机组设备的要求。     

Dynamic wind estimation based control for small wind turbines

We introduce a novel scheme for small wind turbines that gives dynamic estimation of wind speed from rotor angular velocity measurements. The estimation proceeds in two different dynamic observers, one giving a valid estimate for higher Tip Speed Ratios (TSRs) and which we call the Upper Wind Estimator (UWE) and the other called the Lower Wind Estimator (LWE) valid for lower TSRs. The meaning of “higher” and “lower”, and the precise regions of validity, are quantified. We further propose a coordinated control scheme using the UWE. Simulations are presented showing closed-loop performance of the turbine and the estimators both in the optimal TSR regulation condition, and the dynamic power-shedding condition caused by a wind gust. An analytic analysis of closed-loop stability and of the convergence and bias properties of the estimator is provided. Empirical data showing performance on a real turbine is also presented.     

适用于特殊风区的风电机组塔架的再设计

由于IECⅠ级机型性能不能满足超Ⅰ类风区的要求,因此以IECⅠ级机型塔架的结构为基础,对其进行了再设计,并利用有限元方法,对再设计后的塔架的静强度、模态、稳定性进行了分析。分析结果表明:再设计后的塔架的强度、固有频率和刚度均满足要求;以满足强度、频率特性和刚度为约束条件,以减轻重量、降低成本为目标的塔架的再设计是可靠的。     

某风电场风力发电机组故障诊断

为了准确判断风电机组的运行状态及故障,提出了基于常规分析—振动幅值分析—波形频谱分析的故障诊断流程,阐述了针对风电机组的幅值分析方法和波形频谱分析方法,并通过对某机组异响的根源探究实例,准确地诊断出机组异响来源于齿轮箱太阳轮,可为风电机组故障诊断技术提供依据。     

Simulation of hourly wind speed and array wind power

Statistical summaries of wind speed are sufficient to compute many characteristics of turbine-generated power, such as the mean, variance and reliability of various power levels. However, a wind speed time series is necessary to produce a sequence of power values as used for investigating load matching and storage requirements. Since a long historical record of wind speed may not be available at a wind turbine candidate site, it is desirable to be able to generate a simulated numerical sequence of hourly wind speed values. Two such approximate procedures are developed in this paper. One procedure generates sequential wind speed values at a site based on the Weibull parameters of hourly wind speed and the lag-one autocorrelation of hourly wind speed values. Comparison with historical data at a site is made. The second procedure generates sequential hourly wind power values for a regional array of wind turbines. It utilizes the typical site wind characteristics, the spatial and lag-one cross correlation and autocorrelation of hourly wind speed values and an equivalent linearized relationship between array average wind speed and array power. Comparison with results for six different wind turbines in three different regional arrays indicates good agreement for wind power histograms, autocorrelation function and mean persistence.     

Characterization of wind speed data according to wind direction

Knowledge of the wind speed distribution and the most frequent wind directions is important when choosing wind turbines and when locating them. For this reason wind evaluation and characterization are important when forecasting output power. The data used here were collected from eleven meteorological stations distributed in Navarre, Spain. We obtained data for the period extending from 1992 to 1995, with each datum encompassing 10 minutes of time. Wind speed data of each station were gathered in eight directional sectors, each one extended over 45 degrees according to the direction from which the wind blows. The stations were grouped in two blocks: those under the influence of the Ebro valley and those in mountainous areas. For each group the Weibull parameters were estimated, (according to the Weibull probability paper because the Weibull distribution gives the best fit in this region). Kurtosis and skewness coefficients were estimated as well. The Weibull parameters, especially the scale parameter c, depend strongly on the direction considered, and both Weibull parameters show an increasing trend as the direction considered moves to the more dominant direction, while both kurtosis and skewness show a corresponding decreasing trend.     

A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting

The spurt of growth in the wind energy industry has led to the development of many new technologies to study this energy resource and improve the efficiency of wind turbines. One of the key factors in wind farm characterization is the prediction of power output of the wind farm that is a strong function of the turbulence in the wind speed and direction. A new formulation for calculating the expected power from a wind turbine in the presence of wind shear, turbulence, directional shear and direction fluctuations is presented. It is observed that wind shear, directional shear and direction fluctuations reduce the power producing capability, while turbulent intensity increases it. However, there is a complicated superposition of these effects that alters the characteristics of the power estimate that indicates the need for the new formulation. Data from two field experiments is used to estimate the wind power using the new formulation, and results are compared to previous formulations. Comparison of the estimates of available power from the new formulation is not compared to actual power outputs and will be a subject of future work. © 2015 The Authors. Wind Energy published by John Wiley & Sons, Ltd.