基于单机功率间歇性度量指标的风场发电质量评估策略

风电场输出功率的间歇性会对大规模风电安全高效并网利用产生重要影响。本文对比分析了现有单个度量指标对全面刻画风电机组发电功率间歇性存在的不足,提出了一种双度量指标组合的风电机组功率间歇性的综合刻画量方法。在此基础上,对所有风电机组的功率间歇性度量指标序列进行互相关分析,将相关性较高的风电机组聚合在一起形成虚拟风电场;然后,对虚拟风场功率间歇性度量指标序列进行数据驱动建模预报,根据预报结果设置两个度量指标权重,给出可以综合评价未来一段时间虚拟风电场出力质量的系数。实际算例分析表明了上述方法的应用可行性,对风场安全并网和合理弃风具有一定的辅助决策支持作用。     

基于实时功率曲线的风电机组出力特性研究

基于实时功率曲线对风电机组出力特性进行分析,主要通过采集风电机组风速、功率、可靠性状态、大气压力、环境温度等历史数据进行过滤修正,计算出能够反映风电机组真实出力情况的实时功率曲线,并与出质保检测功率曲线进行对比,最后结合功率一致性系数对风电机组出力特性进行评价。找出性能下降比较严重的风电机组并及时整改,进而提高风电机组的发电能力。该方法不仅能够在线评价风电机组的性能优劣,且适用于各类型风电机组,具有广阔的应用前景。     

双重不确定性预测下风电场超短期有功优化控制

针对风电场并网友好性提升问题,提出考虑风速预测不确定性和风电机组有功特性不确定性的风电场发电能力评估方案。对风速超短期预测误差和风电机组在各风速区间的出力特性进行双重不确定性分析并建立概率分布模型,进而利用贝叶斯网络构建风电机组超短期出力的双重不确定性概率预测模型。基于风电场各风电机机组超短期出力概率预测模型,以最大概率跟踪电网调度指令为目标设计场站功率分配策略。算例分析表明,所提考虑双重不确定性的概率预测模型对机风电组有功的概率分布描述更准确,该模型在场站控制中可有效提升电网功率指令的完成水平。     

含风电电力系统机组组合的量子离散差分进化方法

风电具有天然的不可控性和随机性,大量并网给电力系统调度计划带来困难,在电力系统日前机组组合计划中计及风电出力的不确定性,有利于提高电力系统优化运行的精细度。文中通过系统旋转备用将风电出力的预测误差纳入机组组合的数学模型中,为求解含风电的机组组合问题,设计了双层求解方法,外层采用量子离散差分进化法优化传统火电机组的启停状态,内层采用二次规划法优化求解负荷分配的经济调度问题,以提高算法的求解精度。最后通过10机系统算例仿真,验证了文中算法的有效性。情景仿真得出在电力系统日前机组组合阶段中考虑风电出力的波动性和预测误差,可以提高电力系统供电可靠性,也为电力系统运行节省了费用,但随着风电出力预测误差的最大,系统所需旋转备用也会变化,使得发电费用也会增加。     

平均风速时距取值对风电场出力计算的影响

针对风电场平均风速的时距取值与风电机组出力对风速的实际响应时间不匹配的问题,基于风机功率曲线、风电场风速测量及风电机组出力特性,分析了风电场测风频率与风机出力特性的匹配程度及不同的平均风速时距取值对风电场出力计算结果的影响,提出了平均风速时距合理取值的建议。     

数据驱动风电机组偏航误差在线智能识别研究

针对不同风速下风向仪动态特性、风轮尾流、风向仪安装误差等因素导致的风电机组偏航误差问题,文章采用基于运行数据驱动的风电机组偏航误差方法进行在线智能识别。该方法通过改进DBSCAN聚类方法剔除过度离群数据,采用移动最小二乘法拟合“风速-功率-偏航误差”特性曲面,识别出不同风速下的偏航误差曲线,结合在线运行数据采集,可以实现不同风速下偏航误差的动态识别和持续矫正。算例分析表明,与偏航误差设定值相比,在有限数据下识别的偏航误差的识别结果较为准确,且识别误差在合理范围内。该方法的应用能够更为精确识别不同风速下风电机组偏航误差,进一步提高风电机组发电效率。     

考虑风电不确定性的电热系统经济调度

随着风电等新能源大规模并网,其出力的不确定性给电力系统日前调度带来很大挑战。传统的研究方法多是假设风电功率预测误差服从某种概率分布,但风电功率预测误差受到多种因素影响,概率分布模型无法准确描述其特性。为此,采用基于神经网络的组合预测方法对风电功率误差进行建模,再将预测的风电误差加入到包含热电机组、火电机组、风电、储热装置和电锅炉的热电联合优化调度模型中,最后以实际的10机系统为例进行仿真,分析了风电预测误差对机组出力、风电消纳及调度成本的影响。结果表明,与传统方法相比,所建模型可减少机组燃煤成本与旋转备用成本,降低了经济调度成本,提高了风电消纳水平。     

基于相关向量信息熵的风电机组功率曲线构建方法研究

仿真研究湍流强度、空气密度、偏航误差等风电机组功率输出特性关键影响因素与功率曲线之间的内在关联特性,建立各影响因素与功率输出特性之间的隐含关系子模型。结合风电机组运行数据,基于相关向量信息熵技术,实现风电机组运行功率曲线的构建。基于构建的功率曲线与机组实际输出特性开展年发电量对比,结果表明,基于相关向量信息熵法构建的功率曲线能够实现对风电机组出力特性的真实准确评价,评价误差不超过2%。     

基于分段支持向量机的风电机组理论功率计算研究

结合风电机组功率曲线特性与支持向量机学习方法,建立基于分段支持向量机的风电机组理论功率计算模型。研究结果表明,该模型计算精度无论是在单机理论功率计算还是全场理论功率计算方面都要高于目前已有方法。在得到高精度理论功率的基础上,提出一个新的机组性能评价指标——出力可提高系数,该指标能够科学地反映机组设备的运行状态与质量,可为实现风场精细化管理提供依据。     

基于小世界优化的风电功率变权组合预测模型

提出一种新型的基于小世界优化的支持向量机与灰色预测变权组合风电功率预测模型。该模型发挥小世界优化算法避免陷入局部极小、快速收敛等优势,对组合权重系数进行移动样本自适应变权求解,同时,支持向量机采用实数编码小世界算法(R-SWOA)进行回归估计,构成支持向量机改进算法(RSWO-SVM)。利用江苏某风场数据对风电机组输出功率的超短期实时滚动功率预测进行研究,分别预测未来10 min、30 min和1 h的功率值。预测结果表明,无论哪个时间尺度,该文变权组合模型的预测精度均明显高于各单项、等权平均和最小方差固定权系数组合预测方法,预测误差大幅降低。     

基于改进的网格法和BA-BP-ARMA模型的风功率预测

为更精准地预测风功率,首先结合改进的网格法和K均值聚类(Kmeans++)算法预处理风机数据,以剔除异常数据,引入临界概率并根据聚类的实际物理意义设置聚类中心点个数,临界概率同时反映风机性能。其次,利用改进的蝙蝠算法(改进BA)结合前馈(BP)神经网络建立风功率预测模型,BA中引入速度权重因子和高斯变异来避免陷入局部极值。最后,针对风功率模型的预测误差建立自回归滑动平均(ARMA)模型,采用误差的ARMA模型来修正风功率的预测值。结果表明,BA-BP-ARMA组合模型的预测效果更好。研究成果可为风功率预测提供参考。     

Horizontal axis wind turbine working at maximum power coefficient continuously

The performance of a horizontal axis wind turbine continuously operating at its maximum power coefficient was evaluated by a calculation code based on Blade Element Momentum (BEM) theory. It was then evaluated for performance and Annual Energy Production (AEP) at a constant standard rotational velocity as well as at a variable velocity but at its maximum power coefficient.The mathematical code produced a power coefficiency curve which showed that notwithstanding further increases in rotational velocity a constant maximum power value was reached even as wind velocity increased.This means that as wind velocity varies there will always be a rotational velocity of the turbine which maximises its coefficient. It would be sufficient therefore to formulate the law governing the variation in rotational velocity as it varied with wind velocity to arrive at a power coefficient that is always the same and its maximum.This work demonstrates the methodology for determining the law governing the rotational velocity of the rotor and it highlights the advantages of a wind turbine whose power coefficient is always at maximum rather than very variable in line with the variation of wind velocity.     

The effect of wind speed at the top of the tower on the performance and energy generated from _thermosyphon solar turbine

Energy generated from wind turbine depends to a great extent on the wind speed at its inlet. The use of thermosyphon solar tower is an attempt to increase the air velocity at inlet of the wind turbine and of course to increase its power. The wind speed in a certain location changes always with time and with the height above ground surface. In this work, the effect of wind speed at the top of the tower on the performance as well as on the energy generated from thermosyphon solar turbine was studied theoretically. One location in Egypt was chosen for this study. The calculations were achieved mainly with the solar turbine located at tower bottom. For the purpose of comparison, the energy generated from the solar turbine was compared with that generated from free wind turbine at tower height with the absence of solar tower. It was found that, the wind speed at the top of the tower results in a pressure drop which affects the performance of the thermosyphon solar turbine. This pressure drop increases with the rise in wind speed and will be zero only when the wind speed at the top of the tower reaches zero. It was found also that, there is an increase in friction losses through the tower and a decrease in both temperature difference between inlet and outlet of the tower and in heat losses from tower walls with the rise in wind speed in location. The inlet air velocity to the solar turbine and consequently its specific power were found to be increased with the increase in wind speed at the top of the tower. Therefore, the effect of wind speed at the top of the tower must be taken into account during thermosyphon solar tower calculations. By comparing the performance of solar turbine and the free wind turbine located at tower height with the absence of thermosyphon solar tower, it was found that the mean inlet air velocity to the solar turbine located at tower bottom and consequently its specific power are higher than these values for free wind turbine. The mean inlet air velocity to the solar turbine is found to be 117% of its value for a free wind turbine. The yearly specific energy generated from solar turbine is expected to be 157% of its value for free wind turbine.     

Power correlation for vertical axis wind turbines with varying geometries

In this paper, a new predictive model that can forecast the performance of a vertical axis wind turbine (VAWT) is presented. The new model includes four primary variables (rotor velocity, wind velocity, air density, and turbine power output) as well as five geometrical variables (rotor radius, turbine height, turbine width, stator spacing, and stator angle). These variables are reduced to include the power coefficient (C_p) and tip speed ratio (TSR). A power coefficient correlation for a novel VAWT (called a Zephyr Vertical axis Wind Turbine (ZVWT)) is developed. The turbine is an adaptation of the Savonius design. The new correlation can predict the turbine's performance for altered stator geometry and varying operating conditions. Numerical simulations with a rotating reference frame are used to predict the operating performance for various turbine geometries. The case study includes 16 different geometries for three different wind directions. The resulting 48 data points provide detailed insight into the turbine performance to develop a general correlation. The model was able to predict the power coefficient with changes in TSR, rotor length, stator spacing, and stator angle, to within 4.4% of the numerical prediction. Furthermore, the power coefficient was predicted with changes in rotor length, stator spacing, and stator angle, to within 3.0% of the numerical simulations. This correlation provides a useful new design tool for improving the ZVWT in the specific conditions and operating requirements specific to this type of wind turbine. Also, the new model can be extended to other conditions that include different VAWT designs. Copyright © 2010 John Wiley & Sons, Ltd.     

Normalized power curves as a tool for identification of optimumwind turbine generator parameters

This paper presents a novel method of matching wind turbine generators to a site using normalized power and capacity factor curves. The site matching is based on identifying optimum turbine speed parameters from turbine performance index curve, which is obtained from the normalized curves, so as to yield higher energy production at higher capacity factor. The wind speeds are parameterized using cubic mean cuberoot and statistically modeled using Weibull probability density function. An expression for normalized power and capacity factor, expressed entirely in normalized rated speed, is derived. Wind Turbine Performance Index, a new ranking parameter, is defined to optimally match turbines to a potential wind site. The plots of normalized power, capacity factor and turbine performance index versus normalized rated wind speed are drawn for a known value of Weibull shape parameter of a site. Usefulness of these normalized curves for identifying optimum wind turbine generator parameters for a site is presented by means of two illustrative case studies. The generalized curves, if used at the planning and development stages of wind power stations, will serve as useful tool to make a judicious choice of a wind turbine generator that yields higher energy at higher capacity factor     

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。     

基于转速测量的风力机独立控制仿真

风力发电机组的主要部分由风力机和发电机所组成。为了尽可能的尊重实际风力机的物理特性及其运行的物理过程,需要分清控制对象并将风力机与发电机相分离,对其进行独立研究。并通过分析风力发电机组的额定工作点,将额定风速以上的桨叶节距角控制转化为风力机额定转速以上的桨叶节距角控制,最终经实验证实仿真方法实用性与正确性。     

基于混合深度学习的风电功率预测及一次调频应用

为了解决高比例不确定性风电接入电力系统带来强烈调频需求的问题,提出了基于混合深度学习模型的风电功率预测及其一次调频应用方法。首先,采用孤立森林(Isolated Forest, IF)对历史数据进行异常值处理,提高数据质量,其次,构建卷积神经网络(Convolutional Neural Network, CNN)、双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)和注意力机制(Attention Mechanism, AM)的混合深度学习模型对风电功率进行预测。最后,依据功率预测精度配置超级电容器储能,设计储能调频控制原则,弥补风电机组自身预测误差,并协同风电机组参与电力系统一次调频。基于预测结果为4台风电发电机组2个负荷区域仿真系统配置超级电容器储能系统,利用digsilent平台进行了风预测误差和负荷波动下的一次调频仿真。结果表明：所提IF-CNN-BiLSTM-AM模型比BP和LSTM基准模型预测误差(MSE)降低了81.53%和51.44%,具有最优的预测性能;设计的风储一次调频模型与原则可有效应对风电预测误差和负荷波动...     

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.     

Interaction between heterogeneous thermal stratification and wakes of wind turbine arrays

The thermal heterogeneity between the land and sea might affect the wind patterns within wind farms (WF) located near seashores. This condition was modeled with a large-eddy simulation of a numerical weather prediction model (Weather Research and Forecasting) that included the wind turbine actuator disk model (ADM). The assumed condition was that the downstream surface temperature was relatively higher (unstably stratified condition) than the neutrally stratified upstream wind. Under this condition, a thermal internal boundary layer (TIBL) was developed from an area where a step-changed surface temperature was implemented. The combined effect of the wake deficit due to the WF and velocity recovery as a result of enhanced mixing under unstable stratification showed significant modulation of the wind speed at the hub height when local atmospheric stability affected the wind turbine (WT). We show that TIBL height depends on the variables to be evaluated as the threshold. A precise prediction of the TIBL height is beneficial for better estimation of power generation. A prediction model was proposed as an extension of the internal boundary layer (IBL) model for neutral stratification, and the results tracked TIBL development reasonably well. The effects of WFs on surface properties (e.g., friction velocity, heat flux, and Obukhov length) and the tendency of IBL growth were minor. A single WT wake was also assessed under several TIBL developmental stages (i.e., location) and thermal stratification conditions. The standard deviation of the wake deficit increased vertically during the development stage of the TIBL. In contrast, the coefficients in the horizontal and vertical directions were comparable when the WT was deep inside the TIBL.