基于数据分层预处理的短期风功率预测研究

良好的风速和风功率预测是解决风电并网问题的关键。针对样本数据中的无效点影响风功率建模问题,采用分层统计法对风功率进行统计分析后获得了风速—功率关系带,对功率进行修正,根据修正后的数据应用灰色—马尔可夫链模型进行预测,并与比恩法和经验公式法进行对比分析。结果表明,风功率分层统计法可有效地消除坏点数据,预测精度高。     

基于ARMAX-GARCH模型的微电网风功率预测

目前风功率预测多为风功率期望的点预测,且以采样间隔较大的功率序列作为建模序列,这样会降低预测模型对风功率时序特征模拟的准确度和可信度。文中基于小采样间隔风功率序列,提出ARMAX-GARCH风功率预测模型。通过构造风功率新息序列,结合小时平均风功率序列,建立ARMAX点预测模型,采用BIC最小信息准则和相关性分析实现模型定阶和外生变量选择;采用GARCH模型模拟残差的波动特性实现区间预测。以海岛微电网实测风功率数据为例,进行提前1 h风功率预测。结果表明,与持续法、ARMA和RBF神经网络相比,该预测模型能显著提高风功率期望的点预测精度并具有较好的区间预测效果。     

基于可靠性指标LOLP的风电场容量可信度的研究

介绍了基于可靠性指标LOLP的风电场容量可信度计算方法,同时给出了用于风电场可靠性计算的两种模型———发电机模型和负的负荷模型。利用不同的模型,结合实际算例,得出了一系列的计算结果,并对可能影响风电场容量可信度的一些相关因素作了灵敏度分析。     

基于小波变换和时间序列的风功率超短期预测模型研究

为提高风功率超短期预测模型的精确度,利用小波变换将原始风功率时间序列进行分解和重构,得到相应的高频序列和低频序列。对不同序列建立相应的自回归移动平均模型,并且进行拉格朗日乘子检验,验证是否具有拉格朗日乘子效应,从而建立相应的自回归条件异方差模型或广义自回归条件异方差模型,将所得的预测结果进行线性叠加组合得出最终结果。通过算例分析及与其他几种预测模型预测结果的对比,结果表明小波变换和时间序列结合的风功率超短期预测模型可以有效提高风功率超短期预测精度。     

基于短期风功率预测的数据预处理算法研究

针对实测风速和功率数据中包含奇异点以及同一风速下风功率存在较大范围波动的问题,文章提出一种数据预处理算法。首先,采用拉依达准则剔除风速和功率奇异点;再使用优化的一次指数平滑法及最大皮尔逊相关系数对风速进行平滑处理;最后,利用新疆阿勒泰地区某风电场单台风机的实测数据进行验证分析。以文章提出的预处理方法得到的风速作为BP神经网络预测模型的输入,风功率的预测准确度显著高于已有预处理方法得到的结果。     

基于EVMD和布谷鸟算法的短期风功率区间预测

为提高短期风功率预测精度和预测的可控性,提出一种基于能量差优化变分模态分解和布谷鸟优化组合神经网络的短期风功率预测模型。采用能量差优化变分模态分解（EVMD）的模态数,将EVMD用于短期风功率分解,基于EVMD分解序列的不同模态特点,对非线性序列采用布谷鸟优化反向传播神经网络（CS-BPNN）,对平稳序列采用自回归滑动平均模型（ARMA）,并重构加权得到点预测值,并基于EVMD分解所丢失的序列信息构建核密度估计,在点预测模型的基础上,进行风功率的区间预测。将所提预测方法用于澳大利亚风电场的实际算例,实验结果表明,该方法可提高短期风功率预测的准确性。     

考虑风功率预测误差相关性的旋转备用容量优化

文章研究了风电场间风功率预测误差相关性对系统备用容量选取的影响。首先,对不同风电场的风功率预测误差及其相互间的关联特性进行了研究,建立联合概率分布模型;其次,建立了考虑其相关性的旋转备用容量优化模型,模型兼顾经济性与可靠性,以火电系统燃料成本与停电损失之和最小为目标,约束条件着重考虑了系统切负荷与弃风概率均小于设定的置信度;最后,算例验证了模型的有效性,可为系统旋转备用容量的优化制定提供参考。     

基于CGKELM的风功率预测研究

风功率预测在不同应用场景中发挥着越来越重要的作用,从时间尺度上可分为超短期、短期和中长期的风功率预测。基于短期风功率预测对训练时间和预测精度均有较高要求,提出了一种利用共轭梯度(cconjugate gradient,CG)法优化核极限学习机(kernel extreme learning machine,KELM)的方法,即利用共轭梯度核极限学习机(CGKELM)方法来预测风功率,在保证预测精度的前提下,进一步缩短KELM的训练时间。通过利用某风电场的实测数据进行仿真,以均方根误差和相对标准差作为评价指标,将仿真结果分别与反向传播(BP)神经网络、最小二乘支持向量机(LSSVM)和其他KELM方法得到的结果进行比较。研究结果表明：在短期风功率预测方面,CGKELM训练时间比其他方法短,且参数设置简单。该结果证明了CGKELM的有效性,对风电项目的投资决策具有一定的参考价值。     

基于光滑自助法的风功率预测误差核密度建模方法

在风功率预测误差建模应用中,无偏交叉验证(UCV)和经验法则(ROT)是两种常用的非参数方法。然而,由于风功率预测误差中存在的尖峰厚尾,以及局部小样本特征,直接使用这两种方法会产生较大的泛化误差。为了使UCV和ROT在应用中发挥更好的作用,文章提出了一种基于光滑自助法的核密度估计方法。该方法利用了光滑自助法在分位数推断上的优势,通过修改平均积分平方误差(MISE)指标函数,实现了对基本估计方法的校正。该方法本质上是一种装袋方法,可以与任何基本的核密度方法结合使用。在实例仿真中,得到了SBUCV方法和SBROT方法的运行结果,并与UCV和ROT方法的结果进行了对比。仿真结果表明了该方法的有效性。     

多时间尺度下光伏功率波动特性概率分布研究

基于武汉地区分布式光伏电站大量实测数据,运用广义高斯分布和有限学生t混合模型等多种概率模型对不同时间尺度下光伏功率波动特性建模,发现在10～15min时间尺度下广义高斯分布最适用于描述分布式光伏功率变化的概率分布,而在30～60min时间尺度下高斯混合模型拟合效果最好。在此基础上,建立了逐时光伏出力波动与辐射量波动模型,用于定量分析光伏电站能量输出波动,可有效降低光伏功率波动随机性和不确定性对电力系统运行造成的影响,有利于提高光伏并网渗透率。     

我国的风电技术和风电发展

发展风力发电是我国能源战略的一项重要内容.文章首先介绍了我国风电利用的总体状况;然后从风机叶片制造、控制系统及整机制造等方面,详述了现阶段我国风电设备的基本状况;最后对我国风电发展障碍进行了分析,并提出了相关建议.     

考虑测风数据缺失的短期风电功率预测算法

准确的风电功率预测是提高电网稳定性、增加风电场竞争力的重要途径。文章提出了一种以虚拟测风塔技术对测风塔数据进行预处理的方法,对缺失数据进行补全,利用RBF神经网络建立风速预测模型,拟合风速功率曲线得到风电功率预测结果。实验分析显示,基于虚拟测风塔技术的数据预处理方法可有效增加测风数据完整度,提高预测精度,降低风电场的运行维护成本,进一步提高风电场竞争力,具有实际应用价值。     

风力发电系统中组合风速的建模及仿真

在实验室进行风力发电系统模拟,风速模拟是其中重要的一个环节,正确的风速模型不仅可以反应风速实际变化情况,而且能给风力发电系统的模拟研究提供准确的参数。文章采用4分量组合风速模型,用Matlab/Simulink对组合风速进行建模仿真。仿真结果表明,该数学模型能够较精确地反映风速的实际突变性、渐变性及随机性等特点,适用于风力发电系统的模拟研究工作。     

Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.     

Dispatch modeling of a regional power generation system – Integrating wind power

A modeling tool has been developed which can be used to analyze interaction between intermittent wind power generation and thermal power plant generation in a regional electricity grid system. The model uses a mixed integer programming (MIP) approach to determine the power plant dispatch strategy which yields the lowest systems costs. In the model, each large thermal plant is described separately, including properties such as start-up time, start-up cost and minimum load level. The model is evaluated using western Denmark as a case study.For western Denmark, it is found that the inclusion of start-up performance (i.e. start-up time and related costs) and minimum load level of the power generating units have a significant impact on the results. It is shown that the inclusion of these aspects influences the analysis of the effect of wind power variations on the production patterns of thermal units in the system. The model demonstrates how the introduction of wind power production and associated variations change the dispatch order of the large thermal power plants in the western Denmark system so that the unit with the lowest running costs no longer has the highest capacity factor. It is shown that this effect only is detected if start-up performance and minimum load level limitations are included in the optimization. It can also be concluded that start-up performance and minimum load level must be taken into account if the total system costs and emissions are not to be underestimated. The simulations show that if these aspects are disregarded, both total costs and total emissions of the power system are underestimated, with 5% in the case of western Denmark. Models such as the one developed in this work can be efficient tools to understand the effects of large-scale wind power integration in a power generation system with base load plants.     

基于PSCAD的并网型风机建模及仿真

建立了包含风速模型、风力机模型、发电机模型和控制系统模型的风力发电机组的整体动态数学模型;应用PSCAD软件,以建立的数学模型为基础搭建了变速恒频风电机组仿真算例;并以短路故障和渐变风干扰为例,对由一台单机容量为2 MW变速恒频风电机组并网后的运行特性进行了仿真研究.仿真结果表明了变速恒频风电机组良好的运行特性及影响该机型风机稳定的因素.     

Wind power potential and time response of wind energy machines

Low-cost digital wind speed histogram recorders were designed to survey the west coast of British Columbia. Results are presented for several shore and island locations in terms of an available power parameter. Additional short term measurements of autocorrelation and cross-correlation functions showed ten-second exponential correlation in velocity fluctuations and gave values for the root mean square fluctuation. A derivation is given of the response time of a Darrieus wind energy converter, which has implications for the sampling time of any wind speed recorder, and for the power fluctuations to be expected from such a converter.     

基于DIgSILENT的风电储能系统建模与仿真

针对风电功率的随机波动性,采用储能系统改善风电机组的并网运行特性。文章分析了蓄电池储能系统的基本原理及其控制策略,并基于电磁/机电暂态混合仿真程序DIg SILENT/Power Factory搭建了对应的储能系统模型,将其配置在双馈型风电场出口母线的公共连接点处。分别在风速随机波动以及电网侧发生故障的工况下,研究所建储能系统的动态响应特性,结果表明,储能系统可以减小风电输出功率的波动对电网的影响,并能提高风电场并网的稳定性。     

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.     

Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal (year-season-month-day) and spatial scales (wind turbine-wind turbines-wind farm-wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.