Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

A nonlinear wake model of a wind turbine considering the yaw wake steering

Duo to fluctuations in atmospheric turbulence and yaw control strategies,wind turbines are often in a yaw state.To predict the far wake velocity field of wind turbines quickly and accurately,a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions.To consider the shear effect of the vertical incoming wind direction,a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion.This work also developed a“prediction-correction”method to solve the wake velocity field,and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine.Moreover,a 33-kW two-blade horizontal axis wind turbine was simulated using this method,and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics(CFD)simulation results.The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field.Finally,computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8%compared to the experimental values.The established wake model is less computationally intensive than other methods,has a faster calculation speed,and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines.

Wind Potential Modeling at Kanfarandé Site in the Republic of Guinea

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

Cyber-physical Modeling Technique based Dynamic Aggregation of Wind Farm Considering LVRT Characteristic

With the increasing penetration of wind power,large-scale integrated wind turbine brings stability and security risks to the power grid.For the aggregated modeling of large wind farms,it is crucial to consider low voltage ride-through(LVRT)characteristics.However,in aggregation methods,the approximate neglect behavior is essential,which leads to inevitable errors in the aggregation process.Moreover,the lack of parameters in practice brings new challenges to the modeling of a wind farm.To address these issues,a novel cyber-physical modeling method is proposed.This method not only overcomes the aggregation problem under the black-box wind farm but also accurately realizes the aggregation error fitting according to the operation data.The simulation results reveal that the proposed method can accurately simulate the dynamic behaviors of the wind farm in various scenarios,whether in LVRT mode or normal mode.

Variation in cross-shore wind speeds influenced by the morphology of nourished beach

The cross-shore variation in wind speeds influenced by beach nourishment,especially the dramatic changes at the nourished berm,is important for understanding the aeolian sand transport processes that occur after beach nourishment,which will contribute to better beach nourishment project design on windy coasts.In this paper,the influencing factors and potential mechanism of wind speed variation at the edge of a nourished berm were studied.Field observations,together with the Duna model,were used to study the cross-shore wind speed distribution for different nourishment schemes.The results show that the nourished berm elevation and beachface slope are the main factors controlling the increase in wind speed at the berm edge.When the upper beach slope is constant,the wind speed at the berm edge has a positive linear correlation with the berm elevation.When the berm elevation remains constant,the wind speed at the berm edge is also proportional to the upper beach slope.Considering the coupling effects of nourished berm elevation and beachface slope,a model for predicting the wind speed amplification rate at the nourished berm edge was established,and the underlying coupling mechanism was illustrated.

Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas

The joint design criteria of significant wave heights and wind speeds are quite important for the structural reliability of fixed offshore platforms.However,the design method that regards different ocean environmental variables as independent is conservative.In the present study,we introduce a bivariate sample consisting of the maximum wave heights and concomitant wind speeds of the threshold by using the peak-over-threshold and declustering methods.After selecting the appropriate bivariate copulas and univariate distributions and blocking the sample into years,the bivariate compound distribution of annual extreme wave heights and concomitant wind speeds is constructed.Two joint design criteria,namely,the joint probability density method and the conditional probability method,are applied to obtain the joint return values of significant wave heights and wind speeds.Results show that(28.5±0.5)m s^(-1)is the frequently obtained wind speed based on the Atlantic dataset,and these joint design values are more appropriate than those calculated by univariate analysis in the fatigue design.

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily life.For accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few decades.As wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)technology.In this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)method.The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer.In the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)model.To optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios.The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

Wind Speed and Power Density Analysis for Sustainable Energy in Batouri, East Region of Cameroon

This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution model was developed using wind speed data collected from a metrological station at the small Airport of Batouri. Four numerical methods (Moment method, Graphical method, Empirical method and Energy pattern factor method) were used to estimate weibull parameters K and C. The application of these four methods is effective using a sample wind speed data set. With some statistical analysis, a comparison of the accuracy of each method is also performed. The study helps to determine that Energy pattern factor method is the most effective (K = 3.8262 and C = 2.4659).

Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

As a common and extensive datum to analyze wind,wind rose is one of the most important components of the meteorological elements.In this study,a model is proposed to establish the joint probability distribution of wind speed and direction using grouped data of wind rose.On the basis of the model,an algorithm is presented to generate pseudorandom numbers of wind speed and paired direction data.Afterward,the proposed model and algorithm are applied to two weather stations located in the Liaodong Gulf.With the models built for the two cases,a novel graph representing the continuous joint probability distribution of wind speed and direction is plotted,showing a strong correlation to the corresponding wind rose.Moreover,the joint probability distributions are utilized to evaluate wind energy potential successfully.In cooperation with Monte Carlo simulation,the model can approximately predict annual directional extreme wind speed under different return periods under the condition that the wind rose can represent the meteorological characters of the wind field well.The model is beneficial to design and install wind turbines.

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

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

Typhoon wind hazard model and estimation on return period of typhoon wind speed

Typhoons are one of the most serious natural disasters that occur annually on China’s southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to generate 1000-year virtual typhoons for Northwest Pacific basin.The influences of typhoon decay model,track model,and the extreme value distribution on the predicted extreme wind speed were investigated.We found that different typhoon decay models have least influence on the predicted extreme wind speed.Over most of the southeast coast of China,the predicted wind speed by the non-simplified empirical track model is larger than that from the simplified tracking model.The extreme wind speed predicted by different extreme value distribution is quite different.Four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018)and Lekima(2019)were selected and the return periods of typhoon wind speeds along the China southeast coast were estimated in order to assess the typhoon wind hazard.

Wind Speed Forecasting Based on ARMA-ARCH Model in Wind Farms

Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.

A New Method for Calculating the Wind Speed Distribution of a Moving Tropical Cyclone

Based on gradient wind equations, including frictional force, and considering the effect of the movement of a tropical cyclone on wind speed, the Fujita Formula is improved and further simplified, and the numerical scheme for calculating the maximum wind speed radius and wind velocity distribution of a moving tropical cyclone is derived. In addition, the effect of frictional force on the internal structure of the tropical cyclone is discussed. By comparison with observational data, this numerical scheme demonstrates great advantages, i.e. it can not only describe the asymmetrical wind speed distribution of a tropical cyclone reasonably, but can also calculate the maximum wind speed in each direction within the typhoon domain much more accurately. Furthermore, the combination of calculated and analyzed wind speed distributions by the scheme is perfectly consistent with observations.

Poisson-Gumbel Model for Wind Speed Threshold Estimation of Maximum Wind Speed

Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence.Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution.However,few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model.In this study,a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed.We set 0%,5%,10%,20%and 30%gradient thresholds.Then,we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods.The results showed that:(1)When the threshold increases,the maximum wind speed of each return period will decrease gradually.This indicates that the length of the sample series may have a positive effect on the return period wind speed calculation in Gumbel and Poisson-Gumbel methods.Although the augment of the threshold increases the average value of the maximum wind speed of the sample sequence,it shortens the length of the sample sequence,resulting in a lower calculated value of the maximum wind speed.However,this deviation is not large.Taking the common 10%threshold as an example,the maximum wind speed calculation deviation in the 50 a return period is about 1.9%;(2)Theoretically,the threshold is set to make the sample sequence more consistent with Poisson distribution,but this example showed that the effect is worth further discussion.Although the overall trend showed that the increase of the threshold can makeχ2 decrease,the correlation coefficient of linear fitting was only 0.182.Taking Qinzhou meteorological station data as an example,theχ2 of 20%threshold was as high as 6.35,meaning that the selected sample sequence was not ideal.

Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identified and delineated. The composite decision constraint revealed that wind farm projects would not be viable in more than half (57.58%) of the State. Wind speed was the major constraint and accounted for the exclusion of 46.25%, with a mean fuzzy membership value of 0.2008 indicating low suitability across the State. Also, the average acceptable wind farm location for the three-policy scenario was 33.33% of the entire study area. Lafia, Obi, Keana, Awe, Nasarawa-Eggon, Wamba and Kokona LGAs were the identified priority Local Government Areas (LGAs). However, only Lafia, Obi, and Nasarawa-Eggon were consistent with changes in the policy objectives. All the priority LGAs have one or more of the most suitable parcels within their administrative boundaries except for Wamba. Despite the severe limitations of wind speed, substantial parts of Nasarawa State still provide great development potentials for wind energy. The “most suitable” locations in Lafia, Nasarawa-Eggon, and Obi LGAs should have first consideration for the development of wind energy in the State.

Joint probability distribution of winds and waves from wave simulation of 20 years (1989-2008) in Bohai Bay

The joint probability distribution of wind speed and significant wave height in the Bohai Bay was investigated by comparing the Gurnbel logistic model, the Gumbel-Hougaard （GH） copula function, and the Clayton copula function. Twenty years of wind data from 1989 to 2008 were collected from the European Centre for Medium-Range Weather Forecasts （ECMWF） database and the blended wind data of the Quick Scatterometer （QSCAT） satellite data set and re-analysis data from the United States National Centers for Environmental Prediction （NCEP）. Several typhoons were taken into account and merged with the background wind fields from the ECMWF or QSCAT/NCEP database. The 20-year data of significant wave height were calculated with the unstructured-grid version of the third-generation wind wave model Simulating WAves Nearshore （SWAN） under extreme wind process conditions. The Gumbel distribution was used for univariate and marginal distributions. The distribution parameters were estimated with the method of L-moments. Based on the marginal distributions, the joint probability distributions, the associated return periods, and the conditional probability distributions were obtained. The GH copula function was found to be optimal according to the ordinary least squares （OLS） test. The results show that wind waves are the prevailing type of wave in the Bohai Bay.

Projections of Wind Changes for 21st Century in China by Three Regional Climate Models

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020-2029 will be lower compared to those in 1990-1999 in most area of China; 2) annual and winter mean wind speed for 2081-2100 will be lower than those of 1971-1990 in the whole China; and 3) the changes of summer mean wind speed for 2081-2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

Analysis on monthly-averaged distribution of sea surface wind and wave over the seas southeast of Asia using ERS-2 scatterometer data

variation. In the area of 2 The wind system over the seas southeast of Asia （SSEA） plays an important role in China＇s climate this paper, ERS scatterometer winds covering the period from January 2000 to December 2000 and 41°N, 105 130°E were analyzed with a distance-weighting interpolation method and the monthly mean distribution of the sea surface wind speed were given, The seasonal characteristics of winds in the SSEA were analyzed. Based on WAVEWATCH Ⅲ model, distribution of significant wave height was calculated.

Development of wind-energy modeling technology and standards

To ensure the stable operation of power systems with large proportions of wind power,China has published a series of national,industry,and enterprise standards for wind power.The increase in the number of standards and the expansion of their application scope have given rise to a situation where multiple standards overlap and conflict with regard to the establishment of models and their applicability,resulting in unclear standard application scenarios.Therefore,it is imperative to analyze the development of wind-turbine and wind-farm modeling,along with the relevant standards.This paper presents the methods for wind-turbine modeling,the equivalent model of wind farms based on the general model of wind turbines,and the technical provisions and application scenarios involved in the relevant domestic and international standards.The adaptability of the relevant standards is examined.The results of this study are helpful for advancing wind power generation in China and ensuring the safe and stable operation of large-scale wind power systems.