Preliminary results from a network of stations for wind resource assessment at North of Yucatan Peninsula

In order to increase the use of the wind as energy source is essential to study the periodical patterns of the wind resources by means of high quality data obtained from measurement stations, specially configured to evaluate the energy available in the wind. The work presented in this paper shows the preliminary results of a network of ultrasonic wind sensors which were installed and operated by the Autonomous University of Yucatan. Located in the tropical region at Eastern of Mexico, the research zone have been studied previously just by means of wind data measured at local meteorological stations and observatories. The developed measurement network includes six stations with towers between 40 m and 50 m height located around an important region of the North side of the Yucatan Peninsula. The preliminary results have shown a highly directional behaviour of the winds with better wind resources in the sites located on the coastline.     

Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand

The ability of wind power to reliably contribute energy to electricity networks is directly related to the characteristics of the wind resource. An analysis of the characteristics of the wind power resource of the United Kingdom has been carried out, based on modelling of hourly observed wind speed data from 66 onshore weather recording sites for the period 1970–2003. Patterns of wind power availability are presented, with the data demonstrating that the output from large-scale wind power development in the UK has distinct patterns of monthly and hourly variability. The extent and frequency of high and low wind power events is assessed, and wind power data are matched with electricity demand data to examine the relationship between wind power output and electricity demand. It is demonstrated that wind power output in the UK has a weak, positive correlation to current electricity demand patterns; during peak demand periods, the capacity factor of wind power in the UK is around 30% higher than the annual average capacity factor. Comments on the relevance of these findings to modelling the impact of wind-generated electricity on existing electricity networks are given.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Transfer learning for short-term wind speed prediction with deep neural networks

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique.     

Profiling the regional wind power fluctuation in China

As China starts to build 6 10-GW wind zones in 5 provinces by 2020, accommodating the wind electricity generated from these large wind zones will be a great challenge for the regional grids. Inadequate wind observing data hinders profiling the wind power fluctuations at the regional grid level. This paper proposed a method to assess the seasonal and diurnal wind power patterns based on the wind speed data from the NASA GEOS-5 DAS system, which provides data to the study of climate processes including the long-term estimates of meteorological quantities. The wind power fluctuations for the 6 largest wind zones in China are presented with both the capacity factor and the megawatt wind power output. The measured hourly wind output in a regional grid is compared to the calculating result to test the analyzing model. To investigate the offsetting effect of dispersed wind farms over large regions, the regional correlations of hourly wind power fluctuations are calculated. The result illustrates the different offsetting effects of minute and hourly fluctuations.     

Voltage dip generator for wind energy systems up to 5 MW

The increase in installed wind power has brought a number of Grid Code areas into focus. The area of fault ride-through capability is one with serious implications for system security and thus has an impact on the allowed wind energy penetration in the network. There are several wind turbine models that can be used to study the effects of voltage dips and the corresponding wind turbine responses but these models need to be validated by comparing their results with the data obtained during field tests. This paper presents the design of a voltage dip generator that can be used to test wind turbines up to 5 MW and 20 kV. This system is able to adjust voltage dip depth and duration to the standards defined in different countries and also the fault impedance seen by the grid in order not to disturb its operation during the tests. Simulation results are validated using experimental data obtained at a laboratory-scale prototype (400 V, 90 kW). Finally, the actual 5 MW system and the results obtained during field tests are presented.     

Analysis of wind climate and wind energy potential of regions in Turkey

Investments in wind plants have increased rapidly as a result of changes to legal regulations in Turkey over the last five years. This has also led to an increase in the number of wind potential analyses in various regions of the country. This study analyzes the wind climate features of three regions in Turkey and their energy potential. In order to determine the features of wind in these regions, a five-layer Sugeno-type ANFIS model established under the MATLAB-Simulink software was used and the relationship between wind speed and other climate variables determined. In the second phase, WASP software was used to complete the wind energy potential analyses using wind speed data. The final phase includes calculations of the amount of electricity to be obtained technically and capacity usage rates of the installed turbines if wind farms are established in the selected areas. The comparative tables and graphics of the said areas were obtained. In conclusion, the selected areas are well located for the installation of parallel-connected wind plants to the national network in terms of the reliability of wind, the dispersion of wind potential and capacity usage rates.     

Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy

The feasibility of predicting the long-term wind resource at 22 UK sites using a measure-correlate-predict (MCP) approach based on just three months onsite wind speed measurements has been investigated. Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site. The accuracy of the predicted parameters of mean wind speed, mean wind power density, standard deviation of wind speeds and the Weibull shape factor was assessed, and their associated error distributions were investigated, using long-term measurements recorded over a period of 10 years. For each site, 120 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations. Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements. Averaged across 22 sites and all seasons, the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed of 0.21 ms⁻¹ and 4.8% respectively, and in the mean wind power density of 11 Wm⁻² and 14%. The average errors were reduced to 3.6% in the mean wind speed and 10% in the mean wind power density when using the optimum season for onsite wind measurements. These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling. The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment toolkit for small-scale wind developers.     

Can synoptic drivers explain rotor-area wind conditions and predict energy output?

Synoptic-scale weather patterns are an important driver of wind speed at turbine hub height, but wind energy generation is also affected by the wind profile across the rotor. In this research, we use a 6-year record of hourly profile measurements at the Eolos Wind Research Station in Minnesota, USA, to investigate whether synoptic weather patterns can provide information about rotor-area characteristics in addition to hub-height wind speed. We use sea level pressure data from the MERRA-2 reanalysis to classify synoptic patterns at the Eolos site into 15 synoptic types and use the Eolos wind profile data to create mean hourly and mean monthly values of wind speed and turbulence intensity at hub height (80 m), and wind speed shear, wind direction shear, and the potential temperature gradient across the rotor (30–129 m), for each synoptic type. Using a simple linear regression model, we find that, at monthly time scales, wind speed, turbulence intensity, and wind speed shear across the rotor are the most important variables for predicting monthly wind energy output from the Eolos turbine. Regression models using the original Eolos data and the derived synoptic types capture about 64% and 55% of the variance in monthly energy output, respectively. When fewer than the full 6 years of observations are used to fit the regression model, however, predictions using the synoptic types slightly outperform predictions using the Eolos observations. These results suggest that seasonal energy projections may be enhanced by incorporating wind profile measurements with synoptic-scale drivers.     

A comparison of methodologies for monthly wind energy estimation

Monthly wind energy estimations obtained by means of three different methodologies are evaluated. Hourly wind and wind power production data measured at five wind farms in the Northeast of Spain within the period spanning from June 1999 to June 2003 were employed for this purpose. One of the approaches is based on the combined contribution of the hourly wind speed frequency distribution and the corresponding power production. Several alternatives to represent the empirical wind power versus wind speed relationship are considered and their impacts on the error of monthly energy estimations assessed. Two more approaches derive monthly energy estimates directly from monthly wind values: one uses the theoretical power curve to obtain interpolated monthly wind power production values and the other consists in a simple linear regression between the observed wind speed and wind power monthly pairs, which serves as an approximation to the global power curve. The three methodologies reproduce reliably the total monthly wind energy. Results also reveal that linearity is a reasonable assumption for the relation between wind speed and power production at monthly timescales. This approach involves a simplification with respect to other standard procedures that require finer temporal resolution data. Copyright © 2009 John Wiley & Sons, Ltd.     

Combining wind farms with concentrating solar plants to provide stable renewable power

We evaluate the extent to which a combination of wind power and concentrating solar power (CSP) may lead to stable and even baseload power by taking advantage of: 1) spatiotemporal balancing of solar and wind energy resources and 2) storage capabilities of CSP plants. A case study is conducted for the region of Andalusia in Spain. To this end, spatiotemporal variability of modeled CSP and wind capacity factors in a 3-km spatial resolution grid were analyzed based on principal component analysis (PCA) and canonical correlation analysis (CCA). Results reveal that renewable baseload power can be obtained in the study region by locating wind farms and CSP plants using balancing patterns derived from CCA and PCA. In addition, the power fluctuation reduction attained from these patterns was substantially higher than those obtained by interconnecting randomly-located wind farms and CSP plants across the study region. Results were particularly meaningful for the winter season. Upon considering storage capability of the CSP plants, results proved better. The main difference was a higher firm capacity value associated with spring and summer seasons. For the other seasons, the contribution of thermal storage capabilities of the CSP plants to stable power proved less relevant.     

A survey on the assessment of wind energy potential in Egypt

In this paper, wind data obtained from the Egyptian Meteorological Authority are used to assess monthly and annual wind power and wind energy. The study is based on data from 15 anemometer meteorological stations, distributed all over Egypt and covering a period ranging from 1973 to 1994. For these stations the wind data are summarized. The wind energy potential at the 25 m height was obtained by extrapolation of data at 10 m using a power-law expression. The result presents the mean wind energy density estimates and potential for application in Egypt. The analysis showed that along Red Sea coasts, the annual wind energy flux is found to be high, which indicates that these coastal stations are possible locations for wind energy utilization. On both the Mediterranean coast and in the interior parts of Egypt, some stations are of low available wind energy, while others are found to be rather high. Also, the two Weibull distribution parameters have been estimated from the wind speed data for some meteorological stations and the wind power density is calculated using the values of these parameters.     

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods.By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or “NAO”), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation.The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large-scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.     

青海省风能及太阳能资源测试

风能资源是重要的可再生能源,其利用完全依赖于风能资源的数据。UNDP／GEF加速中国可再生能源商业化能力发展项目,在青海开展了风能及太阳能资源实测项目,以进一步促进青海的风能利用。文章介绍了此项目的实施情况,对测得的数据进行了处理,对测试点的风力资源情况做了初步分析。     

风力机叶片覆冰状态监测基准值与分级诊断标准研究

以2 MW风力机为研究对象,基于实际风力机状态(SCADA)系统大数据,选取叶片正常状态和覆冰状态下的风速、功率、桨距角和偏航角数据,采用核密度-均值数据处理方法,得到叶片覆冰状态监测基准值及其定量表达式。同时,根据叶片不同覆冰时期桨距角和功率值随风速的变化情况,提出叶片覆冰状态分级诊断标准。应用结果表明,根据桨距角随风速的变化情况可判断在叶片覆冰过程中机组最大功率追踪情况以及气动性能损失情况,根据风速-功率值分布情况可较准确地判别叶片的覆冰状态。     

Generation Cost Assessment of an Isolated Power System With a Fuzzy Wind Power Generation Model

This paper presents a fuzzy set based modeling of wind power generation. The wind power generation has been solved by the proposed fuzzy generation for an island in Taiwan. The cost effectiveness of wind power generation is then evaluated by calculating the avoided generation cost of diesel generators. The load survey study has been performed to find the typical daily load patterns of various customer classes. With the typical load patterns and total energy consumption by each customer class, the load composition and daily power profile of the isolated power system are therefore derived. The wind power generation of eight wind turbines and the corresponding avoided generation cost is estimated by the fuzzy generation model according to the hourly wind speed. The power generation and the corresponding cost of diesel generators required to meet the system power demand with wind power generation have therefore been obtained. It is found that the wind power generation can economically and effectively substitute the generation cost of the diesel power plant and provide the partial power supply capability for the net peak load demand.     

基于Bagging神经网络集成的风功率预测

鉴于准确预测风功率对风电并网系统安全、稳定运行具有重要意义,提出了基于Bagging神经网络集成的风功率预测模型。先利用拉伊达(3σ)准则对数据进行预处理得到有效的风机数据,结合灰色关联度和Relief算法对数据进行特征提取;其次在Bagging集成学习中使用Bootstrap抽样,随机产生K个训练集并用自组织RBF神经网络(ErrCor-RBF)分别对风功率进行预测;最后叠加K个预测结果取均值得到最终预测结果。仿真结果表明,Bagging神经网络集成的风功率预测模型性能更好、预测精度较高。     

基于随机组合赋权模糊评价的风电机组健康状态评估

由于健康指标权重随机性会导致风电机组状态评估灵敏度降低,提出一种评估风电机组健康状态的随机组合赋权模糊评价方法。首先,通过相关性、方差、偏度等多角度分析风电场采集与监视控制系统（SCADA）数据,结合IEC61400-1标准建立机组健康状态评估指标架构,并基于随机因子优化组合权重得到赋权公式,提高评估指标层权重的准确性。其次,为充分覆盖评估指标数据劣化度,基于岭型分布函数建立健康指标劣化隶属度计算函数。结合随机组合权重和隶属度函数,构建风电机组健康状态模糊综合评价数学模型。通过分层评估风电机组健康状态指标架构,得到机组健康等级并实现故障预警。最后,对大连驼山风电场多台机组进行评估试验,结果表明：该文方法能准确评估出风电机组健康状态等级,相比组合赋权云模型方法,灵敏度提高了1.85%。     

Aeroelastic analysis of a floating offshore wind turbine in platform‐induced surge motion using a fully coupled CFD‐MBD method

Modern offshore wind turbines are susceptible to blade deformation because of their increased size and the recent trend of installing these turbines on floating platforms in deep sea. In this paper, an aeroelastic analysis tool for floating offshore wind turbines is presented by coupling a high‐fidelity computational fluid dynamics (CFD) solver with a general purpose multibody dynamics code, which is capable of modelling flexible bodies based on the nonlinear beam theory. With the tool developed, we demonstrated its applications to the NREL 5 MW offshore wind turbine with aeroelastic blades. The impacts of blade flexibility and platform‐induced surge motion on wind turbine aerodynamics and structural responses are studied and illustrated by the CFD results of the flow field, force, and wake structure. Results are compared with data obtained from the engineering tool FAST v8.