风电机组功率曲线建模方法对比研究

风电机组功率曲线是风电机组重要的性能指标,表征了机组的实际运行状态。准确的实测风电功率曲线可以为风电机组性能评估、风电功率曲线监测、风电功率预测、风电场数值建模等工作提供重要的参考依据。但采用耗时的功率曲线建模方法会花费大量的建模时间,从而影响建模效率。文章从功率曲线建模的数据筛选和功率曲线拟合入手,选取耗时较短的二维核密度估计模型筛选风速功率散点集中区域内的正常运行数据,并选用5种功率曲线拟合方法对正常风速功率数据进行拟合。5种模型的建模精度和建模效率对比分析表明,多项式拟合方法原理简单,拟合速度最快,且拟合精度较高,比较适用于实际功率曲线的建模工作。     

多参数模型风电机组叶片结冰监测与预警研究

详细分析叶片结冰对风电机组运行性能和运行参数的影响,采用功率、叶轮转速和环境温度作为监测叶片结冰的变量。采用高斯过程回归分别建立功率模型和叶轮转速模型实现2个参数的实时监测。引入序贯概率比检验方法分析功率和叶轮转速模型的预测残差以发现2个参数在叶片结冰时的异常变化。当风电机组功率异常、叶轮转速异常且环境温度在0 ℃附近这3个条件同时满足时,发出叶片结冰预警。以云南某高原风场风电机组叶片结冰实际数据为例,验证本文方法的有效性。     

考虑风电机组运行健康状态的风电场优化调度方法

当前风电场功率控制过程中多关注风电机组的发电能力,很少区别对待健康机组和带病机组。对此,以风电机组运行参数为评估指标,计算各指标劣化度,并以综合劣化度为风电机组健康状况的总体评判指标,再结合风电机组启停状态及功率控制要求建立风电场的多目标调度模型。采用混合编码遗传算法优化该模型,获得风电机组启停组合和目标功率值。仿真结果表明,该方法优化了风电机组启停计划,改善了风电机组的整体健康程度,提高了风电场有功输出的可靠性和稳定性,为功率调度过程提供了依据。     

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。     

基于机组来流等效风速计算的风电机组功率曲线修正

风电场功率曲线为工程中常用来描述机舱风速和输出功率之间关系的曲线,与厂商提供的理论功率曲线存在一定的差异。文中根据机舱测风仪的测风误差,提出了一种利用风电机组运行参数来反向迭代计算来流等效风速,拟合其与机舱风速的函数关系,并对理论功率曲线进行修正得到的机舱风速-功率曲线的方法。通过某电站的运行数据来推导来流风速,拟合来流风速与机舱风速间关系,并修正原功率曲线得到机舱风速-功率实际运行曲线,再将得到的机组功率指令来指导机组发电,从而降低机舱风速与来流等效风速存在偏差的影响,提高风电机组的发电效率。     

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。     

基于输出功率波动性的风电机组性能分析方法

输出功率特性是反映风电机组性能的主要指标之一,利用中央监控系统记录的运行数据,提出了3种不同的基于输出功率波动性机组性能分析方法。针对不同型号的机组,在测量机组动态功率曲线的基础上,构建基于功率曲线变异的输出性能分析模型;针对单台机组提出了基于风速和功率方差系数相关性的性能分析模型;针对单台机组运行在不同风速下,提出了基于分段风速的输出性能的分析方法。最后,以2个风电场的机组8个月的运行数据,对提出的机组分析性能方法进行验证,结果表明,文章提出的方法能够有效的分析风电机组的性能。     

基于改进劣化度模型的风电机组日常运行状态评估

为准确地掌握整台风电机组的运行状态,提出一种基于改进劣化度模型的评估方法。首先求得风电机组层次结构中各状态参数的劣化度;其次通过组合赋权法确定各参数的权重,利用岭型分布隶属度函数确定各参数的隶属度矩阵;最后利用模糊综合评判法和彩色图谱对机组的运行状态进行评估和展示。案例机组的日常运行状态评估表明：所提方法可较SCADA系统提前发现机组发电机非驱动端轴承的异常,且误报次数较少。     

基于性能可靠性的风电机组功率曲线评定新方法

针对在实际风电机组验收过程中基于功率曲线的考核评估非常困难的问题,利用中央监控系统的机组运行数据,进行了运行功率曲线的测量,并基于性能可靠性理论,根据制造商承诺功率曲线性能,构建了基于性能可靠度的功率曲线评估方法.利用该方法对北方某风电场5台机组的功率曲线进行了评估.结果表明:5台机组中,6号机组的可靠性最高,4号机组的可靠性最差;该方法评估准确、简单实用,为工程中功率曲线的评价提供了参考.     

Critical analysis of methods for mathematical modelling of wind turbines

Accurate modelling is crucial in designing an optimum system. Wind speed distribution of selected site, hub height and power output curve of chosen wind turbine, are the main factors which influence the performance of wind turbines, and therefore, these must be properly accounted for during modelling of the wind turbines.This paper presents comparative study of various methods for mathematical modelling of wind turbines, with reference to three commercially available wind turbins, with the help of an algorithm developed.It has been found that modelling methods, based on fundamental equations of power available in the wind, are cumbersome to use and do not correctly replicate the behaviour of actual wind turbines.Models based on a presumed shape of power curve, though simple to use, also lack the desired accuracy; however, they give satisfactory response for higher annual average wind speeds.Modelling methods in which actual power curve of a wind turbine is used for developing characteristic equations, by utilising curve fitting techniques of method of least squares and cubic spline interpolation, give accurate results for wind turbines having smooth power curve; whereas, for turbines having not so smooth power curve, model based on method of least squares is best suited.     

Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy

A power curve conventionally represents the relationship between hub height wind speed and wind turbine power output. Power curves facilitate the prediction of power production at a site and are also useful in identifying the significant changes in turbine performance which can be vital for condition monitoring. However, their accuracy is significantly influenced by changes in air density, mainly when the turbine is operating below rated power. A Gaussian process (GP) is a nonparametric machine learning approach useful for power curve fitting. Critical analysis of temperature correction is essential for improving the accuracy of wind turbine power curves. The conventional approach is to correct the data for air density before it is binned to provide a power curve, as described in the IEC standard. In this paper, four different possible approaches of air density correction and its effect on GP power curve fitting model accuracy are explored to identify whether the traditional IEC approach used for air density correction is most effective when estimating power curves using a GP. Finding the most accurate air density compensation approach is necessary to minimize GP model uncertainty.     

On-line monitoring of power curves

A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed.     

Dynamic wind turbine output power reduction under varying wind speed conditions due to inertia

The inertia of wind turbines causes a reduction in their output power due to their inability to operate at the turbine maximum co‐efficient of performance point under dynamic wind conditions. In this paper, this dynamic power reduction is studied analytically and using simulations, assuming that a steady‐state optimal torque control strategy is used. The concepts of the natural and actual turbine time‐constant are introduced, and typical values for these parameters are examined. It is shown that for the typical turbine co‐efficient of performance curve used, the average turbine speed can be assumed to be determined by the average wind speed. With this assumption, analytical expressions for the power reduction with infinite and then finite turbine inertia are determined for sine‐wave wind speed variations. The results are then generalized for arbitrary wind speed profiles. A numerical wind turbine system simulation model is used to validate the analytical results for step and sine‐wave wind speed variations. Finally, it is used with real wind speed data to compare with the analytical predictions. Copyright © 2012 John Wiley & Sons, Ltd.     

一种用激光方法改进风机对风精度的研究

功率曲线是风力发电机组的设计依据,也是考核机组性能、评估机组发电能力的一项重要指标。结合目前已经出现的功率曲线问题的综合分析来看,风机对风精度是影响功率曲线的关键因素之一。现今的风机基本都是利用风向标来进行风机对风。文章通过对现在大量使用的风向标与风机找零方式进行分析研究,提出了一种不受天气、光线影响,更加精确、方便的风向标与风机之间的找零方法,提高了风机对风精度,大大改善了风机的功率曲线。     

Copula‐based model for wind turbine power curve outlier rejection

Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contain significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. Using such raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates, which would make the actual fault detection unreliable and would potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic method for excluding outliers, developed around a copula‐based joint probability model. This approach has the capability of capturing the complex non‐linear multivariate relationship between parameters, based on their univariate marginal distributions; through the use of a copula, data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After filtering the data in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults. Copyright © 2013 John Wiley & Sons, Ltd.     

Performance evaluation of pairing between sites and wind turbines

This paper presents a simple method for determination of pairing between sites and wind generators. It requires six parameters to describe the matching between turbine models and site characteristics, and the energy output performance can thus be easily estimated and used as the index of pairing effectiveness. To describe a Weibull model of wind speed distribution, the shape parameter and the scale parameter are necessarily required. Besides, four other parameters are chosen to specify the characteristics of the power curve of a wind generator: the cut-in speed, the rated speed, the cut-off speed and the nominal power. By combining these six parameters, the average power output of some particular wind turbine at a specific site can be practically and quickly approximated as a reference for turbine siting consideration. An example is also shown to demonstrate the utilization of the proposed method to choose between a group of wind sites and a list of commercial wind turbines.     

基于LabVIEW的离网型风力发电机风速-功率特性研究

通过对离网型风力发电机实际运行状态及气象情况的监测,绘制了风速-功率散点图.利用最大似然估计法对风机输出功率曲线进行拟合,结果表明:七次多项式拟合结果最逼近实际工况;二次多项式拟合曲线精度稍差,但表达式简单明了,可快速预测风力发电机发电量.此外,通过对风力发电机发电量的实际值、理论功率曲线预测值以及利用最大似然估计法拟合得到的功率曲线的预测值的对比,结果表明:风力发电机生产厂商提供的曲线只能反映其稳态的风速-功率关系,用来预测风力发电机发电量还存在较大误差;利用最大似然估计法拟合得到的功率曲线预测的风力发电机发电量误差明显偏小.     

Modeling of the dynamics of wind to power conversion including high wind speed behavior

This paper proposes and validates an efficient, generic and computationally simple dynamic model for the conversion of the wind speed at hub height into the electrical power by a wind turbine. This proposed wind turbine model was developed as a first step to simulate wind power time series for power system studies. This paper focuses on describing and validating the single wind turbine model, and is therefore neither describing wind speed modeling nor aggregation of contributions from a whole wind farm or a power system area. The state‐of‐the‐art is to use static power curves for the purpose of power system studies, but the idea of the proposed wind turbine model is to include the main dynamic effects in order to have a better representation of the fluctuations in the output power and of the fast power ramping especially because of high wind speed shutdowns of the wind turbine. The high wind speed shutdowns and restarts are represented as on–off switching rules that govern the output of the wind turbine at extreme wind speed conditions. The model uses the concept of equivalent wind speed, estimated from the single point (hub height) wind speed using a second‐order dynamic filter that is derived from an admittance function. The equivalent wind speed is a representation of the averaging of the wind speeds over the wind turbine rotor plane and is used as input to the static power curve to get the output power. The proposed wind turbine model is validated for the whole operating range using measurements available from the DONG Energy offshore wind farm Horns Rev 2. Copyright © 2015 John Wiley & Sons, Ltd.