Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

In the present study, wind speed data of Jumla, Nepal have been statistically analyzed. For this purpose, the daily averaged wind speed data for 10 year period (2004-2014: 2012 excluded) provided by Department of Hydrology and Meteorology (DHM) was analyzed to estimate wind power density. Wind speed as high as 18 m/s was recorded at height of 10 m. Annual mean wind speed was ascertained to be decreasing from 7.35 m/s in 2004 to 5.13 m/s in 2014 as a consequence of Global Climate Change. This is a subject of concern looking at government’s plan to harness wind energy. Monthly wind speed plot shows that the fastest wind speed is generally in month of June (Monsoon Season) and slowest in December/January (Winter Season). Results presented Weibull distribution to fit measured probability distribution better than the Rayleigh distribution for whole years in High altitude region of Nepal. Average value of wind power density based on mean and root mean cube seed approaches were 131.31 W/m2/year and 184.93 W/m2/year respectively indicating that Jumla stands in class III. Weibull distribution shows a good approximation for estimation of power density with maximum error of 3.68% when root mean cube speed is taken as reference.

Wind Potential Modeling at Kanfarandé Site in the Republic of Guinea

The purpose of this work is to assess wind potential on the Kanfarandé site (Guinea). The data used for this research covers a period of 6 years (2018 to 2023) and consists of in situ data (Boké meteorological station) and satellite products via NASA Power Larc. The study is based on sorted hourly data (speed and direction). The treatments focus on the monthly, annual and seasonal average of speeds, by sector and their frequencies as well as the annual available powers. The obtained results made it possible, on the one hand, to assess wind potential and, on the other hand, to highlight the most favorable periods for wind energy exploitation. The analyzes show the months of July and August have the best average wind speeds with 5.01 m/s and 5.34 m/s respectively. Average wind speeds are higher during the day than at night with a peak observed at 6 p.m. The study also shows that the prevailing winds are oriented towards the South-West. The Weibull parameters determined for the site give an average of 4.5 m/s for the scale parameter and for the shape parameter 2.40 corresponding to an average power density of 65 w/m2 with an annual available power of 194.80 W/m2 and an annual available energy of 1706.45 kWh/m2.

Investigations into Some Simple Expressions of the Gamma Function in Wind Power Theoretical Estimate by the Weibull Distribution

The Weibull distribution is a probability density function (PDF) which is widely used in the study of meteorological data. The statistical analysis of the wind speed v by using the Weibull distribution leads to the estimate of the mean wind speed , the variance of v around and the mean power density in the wind. The gamma function Γ is involved in those calculations, particularly Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k). The paper reports the use of the Weibull PDF f(v) to estimate the gamma function. The study was performed by looking for the wind speeds related to the maximum values of f(v), v2 f(v) and v3 f(v). As a result, some approximate relationships were obtained for Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k), that use some fitting polynomial functions. Very good agreements were found between the exact and the estimated values of Γ (1+n/k) that can be used for the estimation of the mean wind speed , the variance σ2 of the wind speed v;around the mean speed and the average wind power density.

Near-Surface Wind-Speed Stilling in Alaska during 1984-2016 and Its Impact on the Sustainability of Wind Power

Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increase of 0.0157 m·s–1·a–1, on average, all other first-order weather stations show declining trends in the near-surface wind speeds. In most cases, the average trends are less then?–0.0300?m·s–1·a–1. The strongest average trend of?–0.0500?m·s–1·a–1 occurred at Homer, followed by?–0.0492?m·s–1·a–1 at Bettles, and?–0.0453?m·s–1·a–1 at Yakutat, while the declining trend at Barrow is marginal. The impact of the near-surface wind-speed stilling on the wind-power potential expressed by the wind-power density was predicted and compared with the wind-power classification of the National Renewable Energy Laboratory and the Alaska Energy Authority. This wind-power potential is, however, of subordinate importance because wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency. Since wind turbine technology has notably improved during the past 35 years, we hypothetically used seven currently available wind turbines of different rated power and three different shear exponents to assess the wind-power sustainability under changing wind regimes. The shear exponents 1/10, 1/7, and 1/5 served to examine the range of wind power for various conditions of thermal stratification. Based on our analysis for January 1, 1984 to December 31, 2016, Cold Bay, St. Paul Island, Kotzebue, and Bethel would be very good candidates for wind farms. To quantify the impact of a changing wind regime on wind-power sustainability, we predicted wind power for the periods January 1, 1984 to December 31, 1994 and January 1, 2006 to December 31, 2016 as well. Besides Big Delta that suggests an increase in wind power of up to 12% for 1/7, predicted wind power decreased at all sites with the highest decline at Annette (≈38%), Kodiak (≈30%), King Salmon (≈26%), and Kotzebue (≈24%), where the effect of the shear exponents was marginal. Bethel (up to 20%) and Cold Bay (up to 14%) also show remarkable decreases in predicted wind power.

基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测

近年来,随着碳达峰和碳中和“双碳”战略目标的提出,风力发电已成为可再生能源发电的关键部分。为提高风电功率短期预测的准确度,提出基于密度聚类与自适应噪声完备集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和卷积神经网络与长短期记忆网络结合的短期风电功率预测方法。首先,利用密度聚类将风电功率与天气特征分成不同类别的数据集,通过自适应噪声完备集成经验模态分解算法将不同类别的数据进行频域分解得到子序列分量。以此为基础,将不同的子序列分量与天气特征进行特征选择,输入到卷积神经网络与长短期记忆网络的预测模型。最后,将不同的预测结果进行叠加得到最终的预测结果。整个预测过程通过聚类、分解和特征选择,有效提高了短期风电功率预测的准确度。

考虑训练样本分布不均衡的超短期风电功率概率预测

提出一种考虑训练样本分布不均衡的超短期风电概率预测方法。首先构建深度信念混合密度网络,通过深度信念网络独特的预训练和微调机制提取输入变量的隐特征,利用Beta混合概率分布的有界性准确表征风电预测功率的概率分布,实现隐特征与预测功率概率分布参数之间的非线性映射;然后引入训练样本分布平滑策略,其中特征分布平滑技术用于校准输入特征,标签分布平滑技术用于对各样本误差赋予差异化权重,从输入和输出两方面改善训练样本分布不均衡现象对预测结果的不利影响。实际算例结果表明,与常见风电功率概率预测模型相比,所提模型在点预测和概率预测方面均能获得较高的预测精度,尤其能有效提高低密度样本区域的预测精度。

Analysis of offshore wind energy resources of China

With the economic development, the problems of energy shortage become increasingly severe. As offshore wind energy has advantages, namely it is clean, renewable, not accounting for land area, without noise pollution, with large reserves, etc., which gradually attracts people＇s attention. In this paper, China＇s offshore annual average wind field and monthly average wind field under the mean climate state conditions are obtained, and the corresponding wind density distribution is calculated. China＇s offshore wind energy reserves and distribution conditions through the analysis of wind energy density distribution are summarized, and finally some suggestions for coastal offshore wind energy development and utilization in China are put forward.

大型风电齿轮箱宏观参数优化设计

在大兆瓦级风电齿轮箱设计中,为了提高功率密度同时又尽量减小振动噪声,提出一种齿轮宏观参数优化设计方法。以功率密度、啮合刚度为优化目标,抓住最主要的影响因素,建立多目标优化模型,在模型函数中综合考虑重合度、啮合刚度、齿轮强度与宏观参数设计的关系,得出高效可行的优化方法。对5MW风机高速级齿轮优化结果进行对比,该优化方法在保证齿轮强度条件下,可有效增加啮合刚度,减小传递误差,提高动力学性能,与试验结果对比,降低噪音值4%左右。

新疆达坂城风区顺风向脉动风功率谱

为研究新疆乌鲁木齐所处达坂城风区的复杂脉动风环境,基于实测风场风速数据分析并拟合了该风区的顺风向脉动风功率谱。结果表明:以反比例函数拟合山区峡谷地形平均风速与湍流强度的关系误差较小;山区峡谷地形湍流积分尺度分布较离散,但整体随平均风速的增大而增大;考虑地形系数的Kaimal修正谱在含能区和惯性子区间能较好地表征实测谱,在耗能区选用三参数拟合描述实测谱效果良好;选用过渡函数来描述惯性子区间与耗能区接触区域的功率谱,拟合效果较为理想。可见达坂城风区具有特殊顺风向脉动风特性,以修正谱和拟合谱分段构成的函数模型可用于表征该风区顺风向脉动风功率谱。

低频交流电压下低密度聚乙烯的空间电荷特性

分频输电技术已逐步成为实现碳中和及海上风电远距离传输的主流方案之一。然而,对高压分频输电背景下的电气设备研究较为欠缺,在频率改变对电缆绝缘材料特性影响方面尚需深入研究。为此,采用具备快速检测能力的脉冲电声测试系统,研究了低密度聚乙烯材料在分频电压下不同电场强度、温度时的空间电荷特性以及频率改变对电荷积聚行为的影响。结果表明:当场强及温度条件较为苛刻时,电荷积聚明显;而随着频率的降低电荷积聚量增加,频率下降至20 Hz后,电荷增长速度显著提高。在此基础上,建立了应用于交变电场下的双极型载流子模型,通过仿真计算对实验观察进行了合理化解释,揭示了电场、温度及频率对空间电荷行为的影响机理。研究方法和结果可为海上风电分频输电系统中的频率选择及线缆绝缘优化设计提供理论支撑。

基于WRF模式的四川省凉山州地区风能资源可开发区域研究

利用MERRA2再分析数据驱动WRF模式,对四川凉山州地区2020年全年进行风资源模拟分析,并用凉山州地区典型测风塔数据对模拟结果进行检验,并进行详细地风资源分析,再根据风电场开发8%基准内部收益率反推可开发风能资源的区域分布。结果表明:凉山州大部分地区100 m高度年平均风速在5 m/s以上,风速极大值一般位于山脊,凉山州风能最好的区域主要集中在会东县和宁南县。凉山州典型区域内均表现出受西南季风影响的特征,即冬、春季节风大,夏、秋季节风小,主风向呈强西南风状态,且风功率密度变化规律与风速的变化规律基本一致。凉山州山地区域可开发风能资源的平均风功率密度临界值为258 W/m^(2),这些区域主要集中在会理、会东、宁南、布拖、木里和盐源县境内。可开发区域分布图对指导凉山州地区风能开发提供科学参考。

中国海上油气田风能资源评估

利用持续30年的CCMP分析风场数据,对中国海上油气田开发海域进行了风能资源评估。评估结果表明:不同海区的油气田风能资源分布差异较大,其中东海油气田群的海面风能资源最为丰富,风资源等级可达7级以上,海上油气平台周围10km范围内风能资源技术可开发量可达98~106万kW,风能资源稳定性最好;南海东部油气田群次之,一般可达6级以上,海上油气平台周围10km范围风能资源技术可开发量可达90~104万kW,风能资源月变化和季节变化较强;南海西部油气田群又次之,风资源等级一般可达4级,海上油气平台周围10 km范围风能资源技术可开发量可达58~78万kW,风能稳定性总体月变化较季节变化强,且区域内不同油气田风能稳定性指数数值差异较大;渤海油气田群除渤海南部及中东部外,海面风能资源大多在4级以下,海上油气平台周围10 km范围风能资源技术可开发量一般在42~58万kW之间,风能短期变化较强,月变化和季节变化较弱。相关研究结论可为我国海上油气田海域的海洋清洁能源规划与开发利用提供基础科学依据。

高速高功率密度无轴承永磁薄片电机设计与优化

传统无轴承电机在高速和高功率密度之间难以取得平衡,为此围绕高速高功率密度无轴承永磁薄片电机,对其拓扑结构、绕组形式、主要尺寸等方面进行优化设计,在保证转速和功率密度的基础上有效地提升悬浮性能。通过对运行工况的分析,得出电机拓扑结构需具备的基本特点,提出一种采用环形集成绕组的表贴式无轴承永磁薄片电机。在兼顾转矩和悬浮力输出性能、机械防护可靠性的基础上,对电机的电磁气隙长度和永磁体参数等主要尺寸进行优化设计。针对电机饱和、绕组端部伸出等不利因素,分别采取了超前角弱磁、定子略长的性能提升策略,有效地避免了悬浮力的大幅度跌落。搭建20 000 r/min的无轴承永磁薄片电机有限元仿真模型并制造实物样机。通过对电磁转矩和主、被动悬浮力的分析,证明了本文设计的无轴承永磁薄片电机兼具良好的转矩与悬浮力输出性能,单位幅值电流产生的电磁转矩、主动悬浮力分别为0.166 N·m、4.4 N,电机输出功率10 kW、功率密度5.2 kW/kg。

基于威布尔分布的风功率密度计算方法比较

[目的]风功率密度是风资源评估重要参数之一,准确地计算风功率密度有赖于风频威布尔分布拟合的准确性,对它进行正确地分析和评估有助于降低投资风险和提高投资决策的可靠性。针对目前风资源评估缺少威布尔分布拟合准确性方面的研究,文章旨通过研究比较那种威布尔分布拟合具有较高的精度,从而提高风资源评估的准确性。[方法]对目前国内外采用的5种威布尔模拟风频分布的方法进行研究,引入决定系数来确定威布尔模拟的准确度,比较威布尔函数计算风功率密度与实测数据计算风功率密度绝对误差和相对误差大小。[结果]结果表明:能量因子法EPF和最大似然法MLE模拟出来的威布尔拟合决定系数高于其他方法,包括经验法(EPJ和EPL)和最小二乘法(LLSA)。用这两种方法所得的参数计算风功率密度,与实测数据计算所得的风功率密度相比较,其绝对误差和相对误差也小于其他3种方法。[结论]研究结果可为风资源评估时选择何种威布尔方法计算风功率密度提供参考依据,客观地反应风电场风资源情况,提高风资源评估的准确性。

Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

Air density plays an important role in assessing wind resource.Air density significantly fluctuates both spatially and temporally.But literature typically used standard air density or local annual average air density to assess wind resource.The present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in China.The air density at 100 m height is accurately calculated by using air temperature,pressure,and humidity.The spatial-temporal variation features of air density are firstly analyzed.Then the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,respectively.Usingρstoverestimates the annual wind energy production(AEP)in 93.6%of the study area.Humidity significantly affects AEP in central and southern China areas.In more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are overestimated.Usingρsitesignificantly reduces the estimation error in AEP.But AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in winter.Except for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρsite.Usingρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as well.The impacts of air density on assessing wind resource are almost independent of the wind turbine types.

基于混合自适应核密度估计的风电波动特性分析

分析掌握风电出力波动特性的内在规律有利于提高风电出力的预测精度,进而指导电网调度部门合理安排发电计划,提高系统运行的经济性。为描述风电出力波动的概率密度分布特性,文章首先分别修正由经验法及无偏交叉验证法得到的固定带宽,建立两种自适应带宽核密度估计模型;然后,将上述两种模型进行组合优化;最后,建立基于混合自适应核密度估计(HAKDE)的风电出力波动量概率密度分布模型。采用多种概率密度分布模型对华北某省风电场的不同时空尺度下的风电出力波动量进行拟合,算例结果表明,HAKDE模型的拟合效果最优,从而验证了HAKDE模型的有效性。

基于Weibull分布的风能资源分析与评价——以郑州市为例

基于郑州市8个国家气象观测站2003-2017年所记录的平均风速和风向数据,通过Weibull分布函数计算年均风速、年有效风力小时数、年均风功率密度和风向频率等关键风力技术参数,对郑州市的风能资源状况和风力发电前景进行全面评估。结果表明:郑州地区距地10 m高度的年均风速均呈现逐年减小变化趋势,而历年平均风速从郑州市西部到东部呈现递减的空间分布规律。郑州地区风能资源整体贫乏,但是嵩山地区的风能资源具有显著优势,嵩山地区属于风能丰富区,其平均风速为4.6 m/s、有效风力小时数超过6000 h,平均风功率密度也在200 W/m^(2)以上,且风向频率分布良好,风能分布较为集中,具备很好的经济效益和投资价值。

Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

Vertical Profile of Wind Speed in the Atmospheric Boundary Layer and Assessment of Wind Resource on the Bobo Dioulasso Site in Burkina Faso

This study investigates both the characteristics of the vertical wind profile at the Bobo Dioulasso site located in the Sudanian climate zone in Burkina Faso during a day and night convective wind cycle and the estimation and variability of the wind resource. Wind data at 10 m above ground level and satellite data at 50 m altitude in the atmospheric boundary layer were used for the period going from January 2006 to December 2016. Based on Monin-Obukhov theory, the logarithmic law and the power law made it possible to characterize the wind profile. On the study site, the atmosphere is generally unstable from 10:00 to 18:00 and stable during the other periods of the day. Wind extrapolation models were tested on our study site. Fitting equations proposed are always in agreement with the data, contrary to other models assessed. Based on these equations, the profile of a day and night cycle wind cycle was established by extrapolation of wind data measured at 10 m above the ground. Lastly, the model of the power law based on the stability was used to generate data on wind speed from 20 m to 50 m based on data from 10 m above the ground. Weibull function was used to characterize wind speed rate distribution and to calculate wind energy potential. The average annual power density on the site is estimated at 53.13 W/m2 at 20 m and at 84.05 W/m2 at 50 m, or 36.78% increase. Considering these results, the Bobo-Dioulasso site could be appropriate to build small and medium-size turbines to supply the rural communities of the Bobo Dioulasso region with electricity.