Wind Characteristics Research Articles

Wind field and pressure characteristics on wind turbine surface under moving thunderstorms

Based on computational fluid dynamics and large-eddy simulation technologies, a numerical simulation method of a moving thunderstorm field is established by considering the impinging jet model. In addition, the influence of movement speed of the storm center and jet velocity changes on the characteristics of the wind field is presented. By drawing horizontal wind speed radial profile curves, the vertical profile curves of horizontal wind velocity, radial wind profile curves of vertical wind speed, and transient wind speed characteristics of a moving thunderstorm downburst are illustrated. The results demonstrate that the wind field of a moving thunderstorm exhibits clear asymmetric characteristics. As the height from the ground increases, the horizontal wind speed first increases and then decreases. The extreme value of the horizontal wind speed appears at the position of (0.01–0.03) Djet (where Djet is the jet spout diameter) near the ground, which is consistent with the characteristics of a static thunderstorm wind field. Simultaneously, the distribution characteristics of wind pressure on the surface of wind turbines under moving thunderstorms is further studied. By considering variations of the wind attack angle, jet velocity, and moving speed, the results of wind pressure on the tower and blades of the wind turbine are presented. The coupling of the moving speed and jet velocity has a significant influence on the wind field and pressure on the wind turbine surface.

Characterization of wind speed and directional shear at the AWAKEN field campaign site

The American wake experiment (AWAKEN) is taking place in northern Oklahoma, USA, close to the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) atmospheric observatory. The planning for the deployment of the instruments in this observational field campaign required an assessment of the wind characteristics of the site. This paper analyzes long-term data collected by instruments at the ARM SGP observatory to characterize the winds near the AWAKEN site. The analysis shows that this site experiences high wind shear and veer events with a large number of nocturnal low-level jets. A total of 7086 low-level jet wind profiles over 6 years are examined and found to be dominant from the south and southeast. Significant nocturnal wind veer is observed, which causes southerly wind near the surface to become westerly wind aloft. By identifying a strong relationship between atmospheric stability and wind shear, the wind shear at the site is predicted using the Monin–Obukhov similarity theory (MOST) and validated with the observational data collected by a scanning Doppler lidar. The results show that wind speed at a height of 91 m, a proxy hub height for wind turbines in this area, can be predicted from data collected at a height of 10 m with a bias of −0.35 and 0.65 m s−1 in unstable and stable atmospheric boundary layers, respectively. The bias of the predicted wind speed is mostly in the region of low wind speed, and wind speed above 5 m s−1 at a height of 91 m can be predicted with a bias of less than 0.2 m s−1, and the limitations of the MOST in predicting winds during the stably stratified boundary layer is well-observed.

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

In this quick study, we estimated the Weibull distribution’s parameters using wind data collected between March 2017 and January 2018 using a twelve-meter mast meteorological station on the grounds of the National Energy Research Center in Khartoum. In order to quantify these descriptors, we relied on analytical and stochastic methods, subsequently enabling specialists from researchers, engineers, decision-makers, and policymakers to apprehend the wind characteristics in the vicinity. Hence, the computed scale and shape parameters were provided, in which the Firefly algorithm (FA) resulted in the most accuracy in terms of the coefficient of determination, which equaled 0.999, which we considered logical due to the observed nonlinearity in the wind speed numbers. On the contrary, the energy pattern factor method had the worst prediction capability depending on several goodness-of-fit metrics. This concise work is unique because it is the first to use data from Sudan to forecast local wind speeds using artificial intelligence algorithms, particularly the FA technique, which is widely used in solar photovoltaic modeling. Additionally, since classic estimating approaches act differently spatially, evaluating their efficacy becomes innovative, which was accomplished here. On a similar note, a weighted-average wind speed was found to equal 4.98 m/s and the FA average wind speed was 3.73 m/s, while the rose diagram indicated that most winds with potential energy equivalent to 3 m/s or more blow from the north.

Cluster Partition Operation Study of Air-Cooled Fan Groups in a Natural Wind Disturbance

This study discusses the influence of natural wind on the air flow of air-cooled condensers (ACCs) and then proposes a partition speed-regulation strategy for a fan group with enhanced generalized capability, which is of great practical significance for optimizing energy-saving operations. The stochastic time-varying features of natural wind are characterized by sine–Gaussian, Weibull, and composed winds. In a natural wind disturbance, using the Sugon Supercomputing Center, the transient numerical simulation of the dynamic evolution of the ACC flow field was found: the dynamic system of air flow is a typical time-varying nonlinear process. Cluster analysis was used to extract the nonlinear features of air flow, divide the fan group into four subregions with generalization capability, and implement a partitioned speed operation. It was found that giving priority to increasing the fan speed in the headwind partition can suppress the natural wind disturbance and improve the overall air flow, thus reducing the fan speed in the leeward partition, which reduces the overall air flow loss. The dynamic characteristics of the fan group obtained from the simulation and the proposed fan partition method can guide the optimized energy-saving operation of ACCs.

Eigenanalysis of vector autoregressive model for optimal fitting of a predefined cross power spectral density matrix: Application to numeric generation of stationary homogeneous isotropic/anisotropic turbulent wind fields

Turbulent wind fields representing realistic atmospheric conditions are required for wind turbine load calculations. For a statistically stationary situation, the wind loads on a wind turbine depend, among other wind characteristics, on the spectra and spatial coherences of the velocity field in the rotor plane (i.e. the cross-power spectral density matrix). In this study, a novel approach is proposed to optimally calibrate the parameters of a vector autoregressive model for reproducing a predefined target cross-power spectral density matrix to be used for the synthetic generation of turbulent wind fields. This approach is based on the eigenanalysis of the companion matrix of the vector autoregressive model. In this work, the cross-power spectral density matrix, for different turbulent wind conditions, is considered as targets. This approach is compared to the state-of-the-art approaches for calibration of parameters of vector autoregressive models. The discussion covers the implications derived from approaching the vector autoregressive model determination in the frequency domain or in the time domain.

Application of Multimedia Quality Evaluation Relying on Intelligent Robot Numerical Control Technology in New Energy Power Generation System

New energy (NE) power generation technology has become an important means to solve the problems of global energy demand and environmental governance due to its sustainability and cleanliness. However, restricted by climate and other factors, the instability of its power generation capacity has a serious impact on the voltage multimedia quality evaluation of small microgrids, which even causes damage to important equipment. This paper proposed a multimedia quality evaluation method in the new energy power generation system relying on intelligent robot numerical control technology. According to the state characteristics of different units, the expanded representation models of conventional units, wind turbines, and photovoltaic units were established, respectively. Based on intelligent robot numerical control technology, a multimedia quality monitoring model of the new energy power generation system in random production simulation was established. The planned outage condition of the unit, which was ignored by the current stochastic simulation method, has been fully considered to ensure the rationality of the simulation of intelligent robot numerical control technology. The intelligent robot numerical control technology was used to explore the expected production power change of the unit and to improve the calculation speed and accuracy. In this paper, an appropriate wind and solar power generation model was constructed based on the output characteristics of wind and solar. Secondly, the influence of random faults between units on the subsequent loading rate was studied through probability calculation. The start-stop factors of conventional units were comprehensively analyzed. Then, the expected value of unit start-stop times was obtained. The average relative error of the expected value of the power shortage of the new energy electricity generation system (EGS) was 2.45%, and the error was lower than 2.98%. In the intelligent robot numerical control technology in this paper, the planned outage condition of the unit was considered, which showed the correctness and superiority of the multimedia quality evaluation of the proposed model.

Wind characteristics of Tamil Nadu coast towards development of microgrid - A case study for simulation of small scale hybrid wind and solar energy system

Wind's intermittent nature poses a considerable challenge for an effective microgrid design, particularly in “Island mode.” The objective of this research is to investigate the wind characteristics of a coastal location in Tamil Nadu, India, with a special emphasis on wind consistency, to establish a successful microgrid at the site. The research was based on wind data acquired from an 80 m high meteorological mast established in Manamelkudi, Pudukkottai District, Tamil Nadu, India, from January to December 2017. Based on the analysis, it is observed that, while being within the moderate wind potential regime, the region has a substantially stable wind pattern when compared to the in-land windy places. According to the assessment, the site is expected to effectively complement solar generation, with 74% of its wind generation occurring between 06:00 p.m. and 06:00 a.m. Considering the consistency of wind and the complementary nature of solar generation, the Manamelkudi location is considered to have promising potential for the development of microgrid systems using wind and solar hybrid power. The investigation demonstrated that the geography of the site plays a critical role in the consistent wind pattern of the Manamelkudi site.

Wind resource assessment and influence of atmospheric stability on wind farm design using Computational Fluid Dynamics in the Andes Mountains, Ecuador

In recent years, research has revealed that hydropower designs in Ecuador have not adequately considered sensitivity to climate change. In addition, variations in rainfall patterns cause a drought from July to October. Therefore, it would decrease the flow of the rivers that feed the dams for hydroelectric generation, which would result in a significant reduction of its generating capacity.To prevent this inconvenience from occurring, it is necessary to promote the use of wind energy to diversify the Ecuadorian energy matrix. This matrix is made up of 70% hydropower and 0.26% wind power. Moreover, the Ecuadorian Andes has significant untapped wind potential due to their complex topography. Currently, there are no detailed studies on wind potential or wind energy prediction in the Andes. As a result, wind resource characterization is necessary. In order to address the challenges mentioned above, promote wind energy development, assess wind potential, and diminish the need for thermal generators. This article describes a methodology for wind resource characterization over complex terrain using measured data from two meteorological towers installed in the mountainous zone of study and wind characteristics modeling through the Ansys Computational Fluid Dynamics (CFD) software for the positioning of 11 Goldwind 70/1500 kW wind turbines to optimize the Annual Energy Production (AEP) of a hypothetical wind farm.The study’s main findings indicate that the wind passing between two volcanoes has a high wind potential. This potential is dependent on meteorological variables, orography, and the accelerating effect of wind speed. These favorable conditions make it possible to install a wind farm in this area with 11 high-power wind turbines. Furthermore, the most suitable turbulence model for wind farm design using Ansys Fluent CFD is the K-epsilon which can model with high precision the profile of wind characteristics over a simulated mountain under the influence of atmospheric stability on the state of the air mass to assist in the decision-making process when selecting the best wind farm locations over complex terrain in the Ecuadorian Andes.

Numerical Simulation Research Considering the Influence of Bridge Deck Cross Slope on Wind Performance of π-Type Composite Beam Cable-Stayed Bridge with a Symmetric Elevation Arrangement

Based on a symmetrical cable-stayed bridge, the wind resistance performance of a proposed π-type composite beam is analyzed, and the influence of changing the bridge deck cross slope ratio on the static and dynamic wind characteristics is emphatically studied. The purpose is to provide a design reference for the preliminary wind resistance study of the cable-stayed bridge. The CFD (Computational Fluid Dynamics) numerical calculation method is used to solve the vibration problem of wind-bridge coupling with the help of the software Fluent. It is found that variation of four deck cross slope rates 0%, 1.5%, 2%, and 2.5% has large effects on the static wind coefficient and flutter critical wind speed of a π-type combination beam. In the range of a −3–3° wind attack angle, the static wind drag coefficient will be decreased as the beam deck cross slope rate increases, and in which the drag coefficient at 0% slope rate is the largest. Within the discounted wind speed 13, changing the bridge deck cross slope has little effect on the aerodynamic derivatives of a π-type composite beam. However, the beam deck slope increases the critical wind speed of the bridge, and the critical wind speed at 2.5% is the largest of the four deck slope rates. In addition, it is found that the beam leeward surface keeps generating up and down vortices when the incoming flow wind speed is in the range of 2.5–4.0 m/s, which led the beam to be under vertical vortex vibration, and further research is needed on vibration suppression measures.

Wind and Turbulence Effects on Raindrop Fall Speed

Abstract Wind and turbulence effects on raindrop fall speeds were elucidated using field observations over a 2-yr time period. Motivations for this study include the recent observations of raindrop fall speed deviations from the terminal fall speed predictions (Vt) based upon laboratory studies and the utilizations of these predictions in various important meteorological and hydrological applications. Fall speed (Vf) and other characteristics of raindrops were observed using a high-speed optical disdrometer (HOD), and various rainfall and wind characteristics were observed using a 3D ultrasonic anemometer, a laser-type disdrometer, and rain gauges. A total of 26 951 raindrops were observed during 17 different rainfall events, and of these observed raindrops, 18.5% had a subterminal fall speed (i.e., 0.85Vt ≥ Vf) and 9.5% had a superterminal fall speed (i.e., 1.15Vt ≤ Vf). Our observations showed that distributions of sub- and superterminal raindrops in the raindrop size spectrum are distinct, and different physical processes are responsible for the occurrence of each. Vertical wind speed, wind shear, and turbulence were identified as the important factors, the latter two being the dominant ones, for the observed fall speed deviations. Turbulence and wind shear had competing effects on raindrop fall. Raindrops of different sizes showed different responses to turbulence, indicating multiscale interactions between raindrop fall and turbulence. With increasing turbulence levels, while the raindrops in the smaller end of the size spectrum showed fall speed enhancements, those in the larger end of the size spectrum showed fall speed reductions. The effect of wind shear was to enhance the raindrop fall speed toward a superterminal fall.

Spatiotemporal characteristics of high winds in Jilin Province during 1982–2021

AbstractHigh winds are a type of catastrophic weather characterized by wind speeds over 10.8 m·s−1, occurring in all seasons, and have become more frequent, impacting crop growth, agricultural production and quality of life. It is crucial to understand the spatial and temporal distribution patterns of high winds, as well as their speed and other characteristics, for logical planning of agricultural production and the creation of disaster resilience measures. This study applied the climate tendency, accumulative anomalies, theory of run, Mann–Kendall trend and Theil–Sen median to analyse the spatial and temporal distribution characteristics of the average wind speed and high wind days in Jilin Province, the probability of occurrence of high winds of various magnitudes in different eras, and the gale concentration degree (GCD) throughout Jilin Province for a total of 40 years from 1982 to 2021. The average wind speed and number of high wind days in Jilin Province have decreased over the past 40 years, and this trend has slowed over time. The average GCD in Jilin Province has shown an increasing trend over the last 40 years, with the average GCD being highest in Liouhe and lowest in Linjiang. In general, level six high winds were the main wind hazard level in Jilin Province, with the highest frequency occurring in different seasons and decades. In addition, the occurrence of high wind days is affected by global climate change and different underlying surfaces, which leads to maize and yield reduction. Research on the spatiotemporal distribution of high winds can provide important suggestions for the prevention and management of high wind disasters.

Field Measurements of Wind Microclimate at the Vehicle Level on a Bridge Deck under Typical Canyon Terrain

The wind field characteristics of a bridge site are the key issues for driving the safety assessment condition of mountain canyon terrain. Field measurements on a bridge deck were conducted to investigate the microclimate wind environment at the vehicle level. The measurement points were arranged in the midspan and bridge tower regions along the bridge at vehicle height above the bridge deck. The height-dependent characteristics of the microclimate wind environment at the midspan and bridge tower regions were extensively analyzed. The results show that the mean wind profiles at midspan approximately confirm the power exponential distribution. However, the mean wind profiles show non-power-exponential distribution at the tower regions. The vertical mean wind speed profiles at the midspan and bridge tower regions were then statistically fitted and suggested. The wind speed distribution at the bridge tower regions showed obvious shielding or acceleration effects. Therefore, a typical wind envelope curve across the tower region was proposed along the driving direction. For the turbulence characteristics, turbulence intensities at the tower regions are larger than those at the midspan, and integral scales at the tower regions are smaller than those at the midspan. In this study, a double logarithm third polynomial was recommended to depict the energy distribution of the turbulence microclimate wind in the frequency domain instead of the commonly used empirical spectrum functions. Gust wind speeds were also discussed, and showed that gust wind speeds are more reasonable for evaluating vehicle driving safety and comfort in long-span bridges, especially at the tower regions.

The Use of Local Wind Simulation in Healthcare Facilities for Disease Control

Ventilation and air circulation are essential elements in healthcare facilities. Moreover, good ventilation helps to dilute odour pollutants and control the spread of the disease. This air circulation generally uses mechanical ventilation, whereas the use of natural ventilation is encouraged by the Indonesian Ministry of Health and the World Health Organization due to its affordability and sustainability considerations. Therefore, this paper aims to study the optimal building composition for health facilities using natural ventilation. There are two parts of the discussion in this manuscript. The first part is to study the building shape based on local wind characteristics. The second part is to determine the layout of the interior, adapting to the prevailing wind and its function. The research approach uses venturi theory, solar atrium and cross-ventilation, which is simulated through several computer simulations. The result showed that by orchestrating the building composition and layout in a specific way, natural ventilation could remove disease particles and provide better air circulation. These findings can be a new discussion in the planning and design of health facilities due to natural ventilation.

A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study

The issue of renewable energy curtailment poses a crucial challenge to its effective utilization. To address this challenge, mitigating the impact of the intermittency and volatility of wind and solar energy is essential. In this context, this paper employs scenario analysis to examine the complementary features of wind and solar hybrid systems. Firstly, the study defines two types of complementary indicators that distinguish between output smoothing and source-load matching. Secondly, a novel method for generating wind and solar output scenarios based on improved Generative Adversarial Networks is presented and compared against the conventional Monte Carlo and Copula function methods. Lastly, the generated wind and solar scenarios are employed to furnish complementary features. The testing results across eight regions indicate the proposed scenario generation method proficiently depicts the historical relevance as well as future uncertainties. This study found that compared to the Copula function method, the root mean square error of the generated data was reduced by 4% and 3.4% for independent and hybrid systems, respectively. Moreover, combining these two resources in most regions showed that the total output smoothness and source-load matching level cannot be enhanced simultaneously. This research will serve as a valuable point of reference for planning and optimizing hybrid systems in China.

Wind Characteristics and Temporal Trends in Eastern Paraná State, Brazil

The wind is one of the most important and studied variables globally, essential to several sectors, for example, energy. Therefore, this study assesses the wind regime and analysis trends in three locations within the Paraná state, Brazil. The historical series were recorded between 1976 and 2010 at conventional meteorological stations belonging to the Brazilian National Institute of Meteorology. WRPLOT version 8.0.0 software was used for elaborating wind roses and histograms in the annual and seasonal scales. Detection of trends and temporal rupture points was performed using different statistical methods (Run, Mann–Kendall, Pettitt and Shapiro–Wilk tests) for all meteorological stations. All statistical tests were conducted using the R software version 3.3.2. On a seasonal scale, summer and spring present the highest wind speeds in the Curitiba and Paranaguá stations due to meteorological systems on different scales, such as the South Atlantic subtropical anticyclone and frontal systems. The Mann–Kendall test revealed that Castro presented statistical significance in reducing wind speed, with a decrease of 0.23 m/s per decade for the annual scale and 0.23 m/s per decade during the autumn season. These ruptures indicated a decrease in wind speed in Curitiba and Paranaguá for the spring season. The Pettitt test revealed a break point detection in the data series in Curitiba station, likely due to urban expansion that started in the 1980s, reducing wind speed, especially in winter and spring. These trends and ruptures revealed a significant reduction in wind speed, possibly due to the interaction between natural climate changes and the increase in surface roughness resulting from land use and urbanization changes.

Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry

Geometry alterations of wind turbine blades due to erosion reduce the blade aerodynamic performance, yielding turbine power and energy losses. This study proposes a novel probabilistic analysis framework combining computational fluid dynamics, probabilistic and deterministic uncertainty propagation, and high-performance computing to assess this performance degradation accounting for the unavoidable uncertainty on field records of blade erosion. This uncertainty presently prevents using erosion records for improving wind turbine maintenance planning, increasing energy yield, and thus further reducing the wind energy cost. The technology is demonstrated by quantifying the statistical moments of power and energy yield losses of an eroded utility-scale turbine at a North Sea offshore site and a southern European onshore site. The expectations of the offshore and onshore annual energy production losses are found to be 2 and 3% of the corresponding nominal values, respectively, with corresponding standard deviations of 0.1 and 0.15%. In the realistic scenario of erosion varying with high radial frequency, these low standard deviations result from partial compensation of the impact of mild and severe damages. These low standard deviations indicate that present uncertainty levels of erosion geometry records can be handled with uncertainty analysis in predictive maintenance for further reducing wind energy costs. With the frequent assumption of small or no radial variation of erosion, the standard deviation of the loss is misleadingly higher. For the first time, the study reports on the significant impact of turbulence intensity of the installation site on the turbine loss variability with the site wind characteristics.

High-resolution PV power prediction model based on the deep learning and attention mechanism

Photovoltaic (PV) power generation is associated with volatility and randomness due to susceptibility to meteorological parameters intermittency. This poses difficulty in achieving the desired accuracy of PV power prediction with traditional models. Thus, this paper proposes a new predictive model based on deep learning techniques, optimized by the Bayesian optimization algorithm, to forecast a day-ahead PV power generation in high-resolution time steps. A systematic algorithm is introduced to improve time-series data quality via identifying missing samples in high-frequency datasets and imputing the missing values through the LASSO regression technique. The two data transformers for time and wind features are proposed to enhance their contributions, while other weather information, such as temperature and humidity, are considered. The proposed hybrid model incorporates CNN and BiLSTM to learn spatial and temporal patterns; moreover, the attention mechanism determines the weight values for input series and puts explicit attention on more essential parts to improve accuracy. Finally, the performance of the proposed model is compared with nine deep learning models, which are all optimized by the Bayesian optimization technique. The prediction performance comparison on actual data for a year reveals the superiority of the proposed model with the overall performance of 0,247, 0,232, 1,58%, and 0,461 in MAE, MSE, MAPE, and RMSE, respectively.

ការព្យាករល្បឿនខ្យល់គោលសម្រាប់ការគណនាអគារនៅខេត្តសៀមរាប

A basic wind speed is a very crucial parameter used for calculating the wind loads on building structures. In Cambodia, the selection of the basic wind speed for the structural design remains uncertain due to insufficient fundamental studies on the wind characteristics associated with regional climate. This article aims to predict the basic wind speed in Siem Reap, Cambodia, as a case study based on datasets received from a meteorological station. The wind speeds were measured at Siem Reap airport in the form of hourly speed and then statically converted to 3-second gust speed using Gaussian distribution transformation. The extreme value distributions, such as Gumbel and Gringorten, were utilized to analyze the extreme wind speed in accordance with a return period. The results showed that with a return period of 50 and 700 years, the basic wind speed was found to be 35 m/s and 41 m/s, respectively. These results could provide an alternative selection for the estimation of wind load for the structural design of building in Siem Reap. However, additional studies on the assessment of the existing structures under the discovered wind speeds should be conducted in order to ensure the reliability of the results.

Impact of snow distribution modelling for runoff predictions

AbstractSnow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

Influence of land-sea breeze on PM_{2.5} prediction in central and southern Taiwan using composite neural network

PM_{2.5} prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM_{2.5}.