Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of s...Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of sws in three reanalyses(ERA5,MERRA2,and JRA-55)in East Asia using both satellite and in-situ observations.Results show all three reanalyses can capture the spatial pattern of swS as in observations,yet there are notable differences in magnitude.On land,ERA5 and MERRA2 overestimate the SWS by about 0.6 and 1.5 m s^(-1),respectively,whereas JRA-55 underestimates it.The biases over the oceans are opposite to those on land and are relatively small due to the assimilation of observations of oceanic surface winds.Overall,JRA-55 and ERA5 offer better estimates of seasonal means and variances of SWS than MERRA2.The observed SWS shows a negative trend of-0.08 m s^(-1)/10 yr on land and a positive trend of 0.09 m s^(-1)/10 yr in the western North Pacific.Only JRA-55 shows similar trends to observations over both land and ocean,while ERA5 and MERRA2 show varying degrees of deviation from the observations.Further investigation shows that there is a strong link between the trend of SWS and that of the large-scale circulation,and that a large part of the SwS trend can be attributed to changes in large-scale circulations.展开更多
As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wi...As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.展开更多
Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar revers...Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.展开更多
Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LST...Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.展开更多
One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize...One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.展开更多
Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present ...Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.展开更多
Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement...Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.展开更多
A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate per...The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.展开更多
Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surfac...Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surface layer and PM10 in three representative dust weather processes (dust storm,blowing sand and floating dust) were analyzed.The results showed that wind speed was higher during dust storm and blowing sand with remarkable vertical gradient.The speed in floating dust was relatively lower and increased during the whole process.In general,wind speed after dust weather was smaller with respect to that before the event.The average mass concentrations of PM10 in the processes of dust storm,blowing sand and floating dust were in the ranges of 5 436.38-10 000,1 799.49-4 006.06 and 1 765.53 μg/m3,respectively.展开更多
In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) Th...In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) The wind speed fields in front of fire front generally can be divided into 3 zones and there is always an eddy immediately at the corner between just in front of the fire and the ground. (2) The shape and dimension of the division of the 3 zones is mainly decided by slope angle and ambient wind speed given fire line intensity. (3) There exits an upwind zone in front of fire front. Ambient wind speeds have little effect on the magnitude of the upwind speed when slope angle is 0. But when the slope angle is negative, the upwind is apparently stronger.展开更多
Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference be...Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference between automatic-observed and manual-observed wind speed, including the levels of speed wind, observation instruments and different regions. According to these elements, correction has been conducted, and find that the correction according to the level of wind speed has the best correction effect.展开更多
For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derive...For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.展开更多
This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Techno...This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Technology, the Netherlands. In this experiment, in an attempt to reduce the errors of measurement and improve its accuracy in high-speed flight, some nonintrusive meas- urement techniques, such as the quantitative infrared thermography (IRT), the digital particle imaging velocimetry (PIV), and the s...展开更多
Owing to the advantages of wire-driven parallel manipulator, a new wire-driven parallel suspension system for airplane model in low-speed wind tunnel is constructed, and the methods to measure and calculate the aerody...Owing to the advantages of wire-driven parallel manipulator, a new wire-driven parallel suspension system for airplane model in low-speed wind tunnel is constructed, and the methods to measure and calculate the aerodynamic parameters of the airplane model are studied. In detail, a static model of the wire-driven parallel suspension is analyzed, a mathematical model for describ- ing the aerodynamic loads exerted on the scale model is constructed and a calculation method for obtaining the aerodynamic parameters of the model by measuring the tension of wires is presented. Moreover, the measurement system for wire tension and its corresponding data acquisition system are designed and built. Thereafter, the wire-driven parallel suspension system is placed in an open return circuit low-speed wind tunnel for wind tunnel tests to acquire data of each wire tension when the airplane model is at different attitudes and different wind speeds. A group of curves about the parameters for aerodynamic load exerted on the airplane model are obtained at different wind speeds after the acquired data are analyzed. The research results validate the feasibility of using a wire-driven parallel manipulator as the suspension system for low-speed wind ttmnel tests.展开更多
Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean an...Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal （winter, spring, summer and autumn） wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 （10 yr）-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes （days） also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang （CY）, a station within the central business district （CBD） of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun （MY）. The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 （10 yr）-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure （MSLP） from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.展开更多
A photochemistry coupled computational fluid dynamics （CFD） based numerical model has been developed to model the reactive pollutant dispersion within urban street canyons, particularly integrating the interrelation...A photochemistry coupled computational fluid dynamics （CFD） based numerical model has been developed to model the reactive pollutant dispersion within urban street canyons, particularly integrating the interrelationship among diurnal heating scenario （solar radiation affections in nighttime, daytime, and sun-rise/set）, wind speed, building aspect ratio （building-height-to-street-width）, and dispersion of reactive gases, specifically nitric oxide （NO）, nitrogen dioxide （NO2） and ozone （O3） such that a higher standard of air quality in metropolitan cities can be achieved. Validation has been done with both experimental and numerical results on flow and temperature fields in a street canyon with bottom heating, which justifies the accuracy of the current model. The model was applied to idealized street canyons of different aspect ratios from 0.5 to 8 with two different ambient wind speeds under different diurnal heating scenarios to estimate the influences of different aforementioned parameters on the chemical evolution of NO, NO2 and 03. Detailed analyses of vertical profiles of pollutant concentrations showed that different diurnal heating scenarios could substantially affect the reactive gases exchange between the street canyon and air aloft, followed by respective dispersion and reaction. Higher building aspect ratio and stronger ambient wind speed were revealed to be, in general, responsible for enhanced entrainment of 03 concentrations into the street canyons along windward walls under all diurnal heating scenarios. Comparatively, particular attention can be paid on the windward wall heating and nighttime uniform surface heating scenarios.展开更多
To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the s...To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.展开更多
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
基金supported by the National Natural Science Foundation of China[grant numbers 42361144708,42205041,and 42175165]a scientific research project of the Shanghai Investigation,Design and Research Institute Co.,Ltd.[grant number 2023CN(83)-001]the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab).
文摘Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of sws in three reanalyses(ERA5,MERRA2,and JRA-55)in East Asia using both satellite and in-situ observations.Results show all three reanalyses can capture the spatial pattern of swS as in observations,yet there are notable differences in magnitude.On land,ERA5 and MERRA2 overestimate the SWS by about 0.6 and 1.5 m s^(-1),respectively,whereas JRA-55 underestimates it.The biases over the oceans are opposite to those on land and are relatively small due to the assimilation of observations of oceanic surface winds.Overall,JRA-55 and ERA5 offer better estimates of seasonal means and variances of SWS than MERRA2.The observed SWS shows a negative trend of-0.08 m s^(-1)/10 yr on land and a positive trend of 0.09 m s^(-1)/10 yr in the western North Pacific.Only JRA-55 shows similar trends to observations over both land and ocean,while ERA5 and MERRA2 show varying degrees of deviation from the observations.Further investigation shows that there is a strong link between the trend of SWS and that of the large-scale circulation,and that a large part of the SwS trend can be attributed to changes in large-scale circulations.
基金supported by Basic Science Research Program through the National Natural Science Foundation of China(Grant No.61867003).
文摘As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.
基金supported by the National Natural Science Foundation of China[grant numbers U2442207,42122034,42075043,and 42330609]the Youth Innovation Promotion Association[grant number 2021427]+2 种基金the West Light Foundation[grant number xbzgzdsys-202409]of the Chinese Academy of Sciencesthe Key Talent Projects in Gansu Provincethe Central Guidance Fund for Local Science and Technology Development Projects in Gansu Province[grant number 24ZYQA031].
文摘Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.
基金supported by the National Natural Science Foundation(No.42176020)the Open Research Fund of State Key Laboratory of Target Vulnerability Assessment(No.YSX2024KFYS001)+1 种基金the National Key Research and Development Program(No.2022YFC3105002)the Project from Key Laboratory of Marine Environmental Information Technology(No.2023GFW-1047).
文摘Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.
基金Sponsored by Science Research Project of Liaoning Education Department(Grant No.LJKZ0143)Open Project of State Key Laboratory of Syn⁃thetical Automation for Process Industries(Grant No.2023⁃kfkt⁃01).
文摘One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.
基金supported by the National Natural Science Foundation of China(Grant No.52201379)the Fundamental Research Funds for the Central Universities(Grant No.WUT:3120622898)+2 种基金State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ 231088)Shanghai Key Laboratory of Naval Architecture Engineering(Grant No.SE202305)funded by European Research Council project under the European Union’s Horizon 2020 research and innovation program(Grant No.TRUST CoG 2019864724).
文摘Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.
基金supported by the Major Innovation Project for the Integration of Science,Education,and Industry of Qilu University of Technology(Shandong Academy of Sciences)(Nos.2023HYZX01,2023JBZ02)the Open Project of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD007)+2 种基金the Talent Research Projects of Qilu University of Technology(Shandong Academy of Sciences)(No.2023RCKY136)the Technology and Innovation Major Project of the Ministry of Science and Technology of China(No.2022ZD0118600)the Jinan‘20 New Colleges and Universities’Funded Project(No.202333043)。
文摘Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.
基金supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金The Fund of Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources under contract No.2023CFO016the National Natural Science Foundation of China under contract No.61931025the Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province under contract No.U22A20586.
文摘The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.
基金Supported by the Scientific Project of Jiangsu Environmental Protection(2009008)The Preliminary Research Projects of Jiangsu "Shier Wu" Environmental Protection Planning
文摘Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surface layer and PM10 in three representative dust weather processes (dust storm,blowing sand and floating dust) were analyzed.The results showed that wind speed was higher during dust storm and blowing sand with remarkable vertical gradient.The speed in floating dust was relatively lower and increased during the whole process.In general,wind speed after dust weather was smaller with respect to that before the event.The average mass concentrations of PM10 in the processes of dust storm,blowing sand and floating dust were in the ranges of 5 436.38-10 000,1 799.49-4 006.06 and 1 765.53 μg/m3,respectively.
基金TheresearchissupportedbyFoundationforDoctoralStudiesofMinistryofEducation (No .19980 0 2 2 0 6 )
文摘In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) The wind speed fields in front of fire front generally can be divided into 3 zones and there is always an eddy immediately at the corner between just in front of the fire and the ground. (2) The shape and dimension of the division of the 3 zones is mainly decided by slope angle and ambient wind speed given fire line intensity. (3) There exits an upwind zone in front of fire front. Ambient wind speeds have little effect on the magnitude of the upwind speed when slope angle is 0. But when the slope angle is negative, the upwind is apparently stronger.
基金Supported by Meteorological Data Sharing Center Project (2005DKA31700-01,GX07-01-01)2009 Specific Research in Non-profit Sector (200906041-053)
文摘Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference between automatic-observed and manual-observed wind speed, including the levels of speed wind, observation instruments and different regions. According to these elements, correction has been conducted, and find that the correction according to the level of wind speed has the best correction effect.
文摘For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.
文摘This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Technology, the Netherlands. In this experiment, in an attempt to reduce the errors of measurement and improve its accuracy in high-speed flight, some nonintrusive meas- urement techniques, such as the quantitative infrared thermography (IRT), the digital particle imaging velocimetry (PIV), and the s...
基金National Natural Science Foundation of China (50475099)
文摘Owing to the advantages of wire-driven parallel manipulator, a new wire-driven parallel suspension system for airplane model in low-speed wind tunnel is constructed, and the methods to measure and calculate the aerodynamic parameters of the airplane model are studied. In detail, a static model of the wire-driven parallel suspension is analyzed, a mathematical model for describ- ing the aerodynamic loads exerted on the scale model is constructed and a calculation method for obtaining the aerodynamic parameters of the model by measuring the tension of wires is presented. Moreover, the measurement system for wire tension and its corresponding data acquisition system are designed and built. Thereafter, the wire-driven parallel suspension system is placed in an open return circuit low-speed wind tunnel for wind tunnel tests to acquire data of each wire tension when the airplane model is at different attitudes and different wind speeds. A group of curves about the parameters for aerodynamic load exerted on the airplane model are obtained at different wind speeds after the acquired data are analyzed. The research results validate the feasibility of using a wire-driven parallel manipulator as the suspension system for low-speed wind ttmnel tests.
基金supported by grants from the MOST NBRPC(2009CB421401)CNNSF(41075063) and the CMA Institute of Urban Meteorology
文摘Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal （winter, spring, summer and autumn） wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 （10 yr）-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes （days） also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang （CY）, a station within the central business district （CBD） of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun （MY）. The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 （10 yr）-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure （MSLP） from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.
基金supported by the ICEE of the University of Hong Kong and the Hong Kong Research Grant Council(Project HKU7146/06E)
文摘A photochemistry coupled computational fluid dynamics （CFD） based numerical model has been developed to model the reactive pollutant dispersion within urban street canyons, particularly integrating the interrelationship among diurnal heating scenario （solar radiation affections in nighttime, daytime, and sun-rise/set）, wind speed, building aspect ratio （building-height-to-street-width）, and dispersion of reactive gases, specifically nitric oxide （NO）, nitrogen dioxide （NO2） and ozone （O3） such that a higher standard of air quality in metropolitan cities can be achieved. Validation has been done with both experimental and numerical results on flow and temperature fields in a street canyon with bottom heating, which justifies the accuracy of the current model. The model was applied to idealized street canyons of different aspect ratios from 0.5 to 8 with two different ambient wind speeds under different diurnal heating scenarios to estimate the influences of different aforementioned parameters on the chemical evolution of NO, NO2 and 03. Detailed analyses of vertical profiles of pollutant concentrations showed that different diurnal heating scenarios could substantially affect the reactive gases exchange between the street canyon and air aloft, followed by respective dispersion and reaction. Higher building aspect ratio and stronger ambient wind speed were revealed to be, in general, responsible for enhanced entrainment of 03 concentrations into the street canyons along windward walls under all diurnal heating scenarios. Comparatively, particular attention can be paid on the windward wall heating and nighttime uniform surface heating scenarios.
基金Project(2006BAC07B03) supported by the National Key Technology R & D Program of ChinaProject(2006G040-A) supported by the Foundation of the Science and Technology Section of Ministry of RailwayProject(2008yb044) supported by the Foundation of Excellent Doctoral Dissertation of Central South University
文摘To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.
