Standard Deviation Of Wind Speed Research Articles

Meteorological regime of the Elbrus high-mountain zone during the accumulation period

Unique automated meteorological observations were carried out on the southern slope of Elbrus, near Pastukhov Rocks, at 4700 m a.s.l., during the 2021–2022 accumulation season. Data were obtained on air temperature, humidity, wind speed and direction, snowdrift and radiation fluxes with a temporal resolution of 1 minute or less. Analysis of the data series showed that the representative winter air temperature at this altitude on the southern slope of Elbrus is –10 °С, and the minimum is –36.4 °С; the partial pressure of water vapor does not exceed 3.5 hPa. At the same time, the average daily maximum of wind speed amounted 13.1 m s–1 with the absolute maximum of 54.1 m/s. Snowstorms with a snow transport intensity of more 0.1 kg/m2s–1 are quite common phenomenon in winter, while the maximum average value of the transport reaches 0.87 kg/m2s–1. An empirical relationship was established between the average hourly wind speed and the maximum gust speed for the same period, and it was shown that for these conditions the wind gust exceeds the average hourly wind speed by 1.8 times, while the representative value of the standard deviation of wind speed is 5.8 m s–1. This information may be useful not only for the glaciologic problems and modeling, but also for construction and engineering surveys, which are relevant in view of the present-day active development of the mountain ski infrastructure on the southern macro-slope of the Elbrus. In addition, the obtained series of instrumental observations were used to assess the quality of reanalysis data for high mountain regions taking as an example the ERA5. The ERA5 reanalysis was demonstrated to reproduce rather successfully the air temperature, wind speed and humidity in high mountain conditions, but extreme values for all these parameters are underestimated. Thus, the minimum temperature in winter turned out to be overestimated by 2 °C, and the maximum was underestimated by 4 °C, while the wind speed, according to the ERA5 reanalysis, never exceeded 40 m/s during the above observation period. It is also shown that the FlowCapt4 acoustic blizzard gauge (driftometer) can be used to estimate average wind speeds since it is less sensitive to severe high-altitude conditions compared to acoustic and cup anemometers.

Enhancing accuracy of surface wind sensors in wind tunnel Testing: A Physics-Guided neural network calibration approach

Irwin’s surface wind sensor is widely used in wind tunnel testing for urban and environmental aerodynamics studies. However, the conventional physics-based calibration of this sensor could result in reduced measurement accuracy in regions with low flow velocities and high turbulence intensity. To address this issue, this study proposes a novel physics-guided neural network (PGNN) calibration approach, which couples a physics-based calibration model, derived from extended Taylor series expansions of measured wind speed, with an adaptive, data-driven general regression neural network. Sensors are calibrated within the turbulent boundary layer of an empty flat plate, considering both mean and standard deviation of wind velocity measured by high-accuracy thermal anemometry. The accuracy of calibrated sensors is then assessed using a 1:400 benchmark urban model. Experimental results show significant improvement in measurement accuracy, reducing mean absolute percentage error for wind speed standard deviation from 92.3 % with the current model to 9.8 % using PGNN.

Evaluating ERA5 reanalysis predictions of low wind speed events around the UK

Low wind speed events represent one of the biggest challenges in fully de-carbonising the electricity system due to the growing proportion of wind energy in the UK energy mix. While reanalysis products are a useful tool to study the spatio-temporal characteristics of these occurrences, their performance and limitations should be understood prior to usage. In this study, hourly 10 m ERA5 reanalysis wind speed data were evaluated against in-situ wind speed measurements from 205 onshore and offshore observation stations around the UK. It was found that ERA5 has biases in mean wind speed of 0.166 m/s and −0.136 m/s for onshore and offshore domains respectively, and biases in hourly wind speed standard deviation of −0.354 m/s and −0.425 m/s for onshore and offshore domains respectively. Both errors are more pronounced in autumn and winter. These errors lead to an underestimation of the percentage frequency of short-duration low wind speed events. Furthermore, the findings suggest that the largest errors are from sites which are situated in coastal and mountainous regions where short-range topographical variability and local wind effects may not be resolved by ERA5. Despite these shortcomings, ERA5 nevertheless outperforms its global reanalysis counterparts in the UK domain and therefore, can provide valuable information in the context of low wind speed events prediction.

Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation

The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.

Correction of WindSat sea surface wind speed under rain

To improve the accuracy of sea surface wind speed under rain based on data obtained from the U.S. spaceborne fully polarimetric microwave radiometer (WindSat), a wind speed correction model based on a statistical algorithm is constructed. First, the coupled samples from 2003 to 2014 are considered as the training sample set, and the coupled samples from 2015 to 2020 are used as the test sample set. Then, the WindSat sea surface wind speed under rain correction model is constructed. Results show that the WindSat wind speed data is overestimated severely. The higher the wind speed, the larger the wind speed is overestimated. The experimental results reveal that the WindSat wind speed standard deviation of the test samples is 2.6 m/s. However, the wind speed standard deviation decreases to 2.2 m/s after correction.

Large eddy simulation of atmospheric boundary layer flow over complex terrain in comparison with RANS simulation and on-site measurements under neutral stability condition

Large eddy simulation (LES) of the atmospheric boundary layer (ABL) flow over complex terrain is presented with a validation using meteorological tower (met-tower) data through an improved neutral stability sampling approach. The proposed stability sampling procedure includes a condition based on the most-likely occurrence time-periods of the neutral ABL and reduces the variabilities of the conditional wind statistics calculated at the met-towers in comparison to our previous work. The ABL flow simulations are carried out over a potential wind site with a prominent hill based using the OpenFOAM-based simulator for on/off-shore wind farm applications by applying the Lagrangian-averaged scale-invariant dynamic sub-grid scale turbulence model. A low-dissipative scale-selective discretization scheme for the non-linear convection term in the LES governing equation is adopted implicitly to ensure both the second-order accuracy and bounded solution. The LES inflow is generated through a precursor method with a “tiling” approach based on the flow driving parameters obtained from a corresponding Reynolds-averaged Navier–Stokes (RANS) simulation. Overall, the averaged wind velocity profiles predicted by the LES approach at all met-tower locations show a similar tendency as the RANS results, which are also in reasonable agreement with the met-tower data. An obvious difference in wind speed standard deviation profiles is seen between LES and RANS, especially at regions downstream of the hill edge, where the LES shows under-predicted results at the highest measurement levels in comparison to the tower data. The computational costs of the LES are found to be about 20 times higher than the RANS simulations.

Evaluation of Sea Surface Wind Products from Scatterometer Onboard the Chinese HY-2D Satellite

The Chinese new marine dynamic environment satellite HY-2D was launched on 19 May 2021, carrying a Ku-band scatterometer (named HSCAT-D). In this study, wind products observed by the HSCAT-D were validated by comparing with wind data from the U.S. National Data Buoy Center (NDBC) buoys and European Centre for Medium-Range Weather Forecasts (ECMWF) model. The statistical results show that the HSCAT-D winds have a good agreement with the buoys’ wind measurements: in comparison with buoy winds, the wind speed standard deviation (STD) and root-mean-squared errors (RMSE) of direction were 0.78 m/s and 14.10°, respectively. Other scatterometers’ wind data are also employed for comparisons, including the HY-2B scatterometer (HSCAT-B), HY-2C scatterometer (HSCAT-C), and MetOp-B scatterometer (ASCAT-B) winds. The statistical results indicate that errors for HSCAT-D winds are smaller than HSCAT-C but a little bit larger than HSCAT-B. The spectral analysis shows that the HSCAT-D wind products contain less small-scale information than ASCAT-B. Moreover, the Extended Triple Collocation (ETC) results show that the HSCAT-D wind product is of good quality and well-calibrated. We believe that the HSCAT-D wind products will be helpful for the scientific community, as shown by the encouraging validation results.

The impacts of assimilating CFOSAT scatterometer winds for Typhoon cases based on real-time rain quality control

Rain is the main factor influencing the Ku-band scatterometer wind quality. The operational quality control (QC) scheme of the scatterometer wind retrieval procedure cannot effectively distinguish rain from high winds, limiting the optimal assimilation of Ku-band scatterometer winds in numerical weather prediction. To compensate for the inadequate operational QC of CFOSAT scatterometer (CSCAT) data, a real-time rain quality control (RRQC) method in data assimilation was proposed. Based on real-time rain forecasts, the RRQC method aims to screen and eliminate rain-affected CSCAT data. To begin, the impact of rain on CSCAT data for the entire year 2019 was analyzed. The CSCAT wind speed standard deviation (STD) and bias are found to increase with rain rate, and when the rain rate exceeds 2.5 mm/h, the STD is greater than the CSCAT-designed accuracy (2 m/s). Following that, two Typhoon cases, namely Lekima (2019) and In-fa (2021), were studied to assess the RRQC method's impact on Typhoon forecasting. It shows that assimilating CSCAT data with the RRQC method improves wind analysis and forecast, and reduces the mean track error. Finally, Typhoon Lekima was further diagnosed. It is found that the CSCAT data eliminated by RRQC are mainly distributed around the Lekima rainband with large wind speed observation minus background (OMB) absolute values. The results indicate that RRQC can identify rain contaminations in the CSCAT data and thus improve the analysis and forecast.

SHORT-TERM WIND SPEED FORECASTING USING DEEP VARIATIONAL LSTM

Wind speed and power at wind power stations affect the efficiency of a wind farm, so accurate wind forecasting, a nonlinear signal with high fluctuations, increases security and better efficiency than wind power. We are looking for wind speed for a wind farm in Iran. In this research, a combined neural network created from variational autoencoder (VAE), long-term, short-term memory (LSTM), and multilayer perceptron (MLP) for dimension Reduction and encoding is proposed for predicting short-term wind speeds. The data used in this research is related to the statistics of 10 minutes of wind speed in 10- meter, 30-meter, and 40-meter wind turbines, the standard deviation of wind speed, air temperature, and humidity. To compare the proposed model (V- LSTM-MLP), we implemented three deep neural network models, including Stacked Auto-Encoder (SAE), recurrent neural networks (Regular LSTM), and hybrid model Encoder-Decoder recurrent network (LSTM-Encoder-MLP) presented on this dataset. According to the RMSE statistical index, the proposed model is worth 0.1127 for a short time and performs better than other types on this dataset.

A Motion-Correction Method for Turbulence Estimates from Floating Lidars

Estimates of atmospheric turbulence performed by both fixed and floating vertically profiling, conically scanning wind lidars are affected by the measurement volume and turbulence structure, among others. We study this phenomenon by simulating the lidar measurements within synthetic fields of atmospheric turbulence. We use the simulations’ framework to assess the impact of buoy motions on turbulence estimation. Simulation results show that the buoy’s translational motions impact turbulence estimates the most. We also apply the simulation framework to analyze measurements from a floating lidar measuring nearby an offshore meteorological mast for a period of six months. The analysis of measurements is presented both without and with motion compensation. In general, we find from both simulations and measurements that the buoy motions do not impact the mean horizontal wind speed significantly, in agreement with previous studies. However, both simulations and measurements show that the standard deviation of the horizontal velocity is overestimated by the floating lidar. When we correct the measurements based on compensation factors derived from the simulations, the mean bias of the horizontal wind speed standard deviation changes from 18–19% to 5–21%, with large reductions at the first four heights closest to the surface and a slight increase at the highest vertical level.

A COMPARATIVE ASSESSMENT OF MACHINE LEARNING MODELS FOR PREDICTING WIND SPEED

Renewable energy is one of the most critical issues of continuously increasing electricity consumption which is becoming a desirable alternative to traditional methods of electricity generation such as coal or fossil fuels. This study aimed to develop, evaluate, and compare the performance of Linear multiple regression (MLR), support vector regression (SVR), Bagging and random forest (R.F.), and decision tree (CART) models in predicting wind speed in Southeastern Iran. The data used in this research is related to the statistics of 10 minutes of wind speed in 10-meter, 30-meter, and 40-meter wind turbines, the standard deviation of wind speed, air temperature, humidity, and amount of the Sun's radiation. The bagging and random forest model with an RMSE error of 0.0086 perform better than others in this dataset, while the MLR model with an RMSE error of 0.0407 has the worst.

Reliability Evaluation of Nondispatchable Energy Sources in Generation Planning: A Wind Electrical System Case Study

Summary. Generation planning is an important aspect of the probabilistic production simulation (PPS) process for power systems. The inclusion of nondispatchable energy sources in the conventional system needs efficient algorithms for reliability evaluation. Moreover, it is crucial to represent the availability of nondispatchable energy sources with a suitable probability distribution function (PDF) that can be easily integrated with the conventional system. The present work extends the generation reliability to include nondispatchable energy sources in the reliability evaluation methodology. The wind electrical system (WS) is represented using a simple wind speed model based on the normal distribution function. The multistate wind speed model based on the mean and standard deviation of wind speed at a particular site is integrated with the conventional system for reliability study. The proposed fast Fourier transform (FFT)-based method eliminates extensive calculations encountered in the conventional approach due to cumbersome time-domain convolution. The efficacy of the methodology is validated with the case studies using the IEEE RTS-wind system with loss of load probability (LOLP) and expected energy not served (EENS) reliability indices.

A one-year long turbulence simulation using a WRF-LES based modeling system at Østerild

Here we show an intercomparison between turbulence simulations and measurements performed at a 250-m tall meteorological mast located at the wind turbine test center of Østerild in northern Denmark. The Weather Research and Forecasting model is used to perform the simulations. We use the model’s nesting capability to downscale the forcing reanalysis down to sub-kilometer domains, which are run in a large-eddy simulation fashion, thus being able to partly resolve turbulence. We find that the simulations are able to reproduce accurately the wind climatology of the site when compared to the observations at the mast. The wind power densities derived from the time series of 10-min mean wind speeds from simulations and observations differ by less than 2%. The observed turbulence conditions, in the form of histograms of the wind speed standard deviation, seem fairly well reproduced by the simulations, although there seems to be a tendency of lower simulated turbulence levels. This is also partly reflected when studying the behavior of the turbulence intensity with increasing mean wind speeds. Spectral analysis on the mean wind speed time series reveals that the simulations are able to reproduce the energy content of the observations rather well, but we find a slight larger variability of simulated winds at frequencies larger than 1 h.

Extreme wind speed ramp events: A measurement-based approach for improving the modelling of ultimate loads for wind turbine design

Ramp events, i.e., significant changes in wind speed in a short time period, have become critically important to end-users. However, only a few studies address their impact on wind turbine loads. To the best of the authors’ knowledge, these results have not yet been validated with measurements. Therefore, this paper aims to investigate the impact of extreme wind speed ramps on ultimate wind turbine loads using eight months of offshore measurements. We also compare the measured loads with simulations following the International Electrotechnical Commission (IEC) extreme turbulence model, in order to improve the modelling of ultimate loads. This is because events with a 10-min horizontal wind speed standard deviation higher than the prescribed IEC turbulence class, in line with other research, are primarily associated with ramp events. They are found to be design driving for the blade root flap-wise moments below and beyond rated wind speed, but not in the transition region. The high-frequency analysis of these moments showed a sudden pitch transition from the inactive to the active region. In general, the loads associated with ramp events did not exceed the simulations. In addition, non-ramp related extreme loads around rated wind speed, which exceeded the simulations, were associated with standard deviations slightly above the normal turbulence model (NTM) of IEC for a waked turbine, indicating the impact of wake added turbulence. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden pitch transition from the inactive to the active region in addition to wake added turbulence.

Determination of mixing layer height from co-located lidar, ceilometer and wind Doppler lidar measurements: Intercomparison and implications for PM2.5 simulations

Mixing layer height (MLH) is retrieved at an urban site in Seoul, Korea, from 2-year collocated remote sensing instrument measurements: elastic aerosol lidar, ceilometer, and wind Doppler lidar (WDL). All three measurements captured clear diurnal patterns of MLH development and break down, but large discrepancies were noted in the nocturnal MLH and the onset time of MLH development. Vertical wind speed standard deviation (σw) from WDL measurements displayed low MLHs during nighttime, compared to MLH retrieved from aerosol backscattering intensity profiles (mean bias = −0.21 km). To this extent, mixing represented by σw showed close correlation with surface heating but significant discrepancies with the vertical distribution of atmospheric constituents, especially at night. Comparison of MLH from elastic aerosol lidar and WDL with WRF-Chem simulation results showed that WRF-Chem MLH closely resembled MLH retrieved from WDL, indicating that WRF-Chem sufficiently simulated turbulence, but displayed lower nocturnal MLH compared to lidar-derived MLH. Furthermore, investigation of the vertical profiles of PM2.5 in the model displayed confined distribution of aerosols within the nocturnal MLH, leading to overestimation of surface PM2.5 concentration. For the case studies of May 12 and 18, 2016, implicating full mixing within the MLH defined by aerosol lidar measurements on WRF-Chem PM2.5 vertical distribution simulations was shown to significantly reduce WRF-Chem PM2.5 simulation errors from mean normalized bias of 52% to 19%.

Modelling Wind Energy Potential in Different Regions with Different Methods

Processing a lot of data is a very difficult and laborious task. In order to save time and ease the process, computational intelligence method is a very practical method for data processing. In the present study, the potential of wind energy in different regions of Turkey based on the hourly wind speed data in the years 2008-2017 were analysed statistically. Wind power density values have been examined mathematically and statistically and modelled using artificial intelligence methods. During the statistical analysis, maximum wind speed, average wind speed, wind power density, and standard deviation of wind speed have been determined. The cumulative Weibull function was used to determine wind power density and wind speed distribution on an annual basis using hourly wind speed data. Predictive models have been created by using machine learning algorithms which are computational intelligence method for the obtained wind power density values. Decision tree (DT) algorithm and multilayer perceptron (MLP) algorithm have been chosen as machine learning algorithms. Four different error analyses have been performed for DT and MLP estimates. In the machine algorithms used to estimate wind power values, the DT algorithm performed approximately 35% more accurate than the MLP algorithm. As a result, wind power densities for certain regions have been determined by using both mathematical model and computational intelligence methods.

Study of the pitch behaviour of large‐scale wind turbines based on statistic evaluation

Pitch control is a basic function of large-scale wind turbines to improve energy capture efficiency. Pitch system is also a component system with a high failure rate due to the complex pitch behaviour. It is helpful to the practical design and control if a clear pitch behaviour is obtained. However, pitch behaviour is affected by the structure of wind turbines, control strategies, wind resource characteristics, and so on. So, there is still a lack of sufficient understanding. In this study, the pitch behaviour of a 2 MW wind turbine is investigated based on SCADA data. Four key evaluation indicators describing pitch behaviour are proposed, which include pitch status, pitch time consumption, pitch angle peak, and mean pitch angle. Moreover, the SCADA data extraction algorithm for pitch behaviour investigation is also given. Then, the pitch behaviour is investigated based on statistical evaluation. Some valuable conclusions have been given. For example, for the investigated wind turbine, the time proportions for the four states (non-pitch, increasing pitch, pausing pitch, and decreasing pitch) are 85.80%, 5.97%, 1.45%, and 6.78%, respectively. The pitch cycles, pitch angle peak, and mean pitch angle increase with the increase of the standard deviation of wind speed.

Scatterometer Sea Surface Wind Product Validation for HY-2C

The Chinese HY-2C satellite was launched on Sep. 21, 2020, carrying the new Ku-band scatterometer (HSCAT-C). Different from the other currently operating scatterometers, the HSCAT-C is in a nonsun-synchronous orbit and as such it will be useful for the cross-calibration of sea surface wind products from current space-borne scatterometers and radiometers. In this article, the HSCAT-C wind products are validated by comparing to collocated winds from buoys, European Centre for Medium-Range Weather Forecasts model, and several other scatterometers. The results show that the quality of HSCAT-C winds is very good: in comparison with buoy winds, the wind speed standard deviation and direction root-mean-square error are 1.03 m/s and 15.9°, respectively. The HSCAT-C winds show very good agreements with the HSCAT-B winds, especially in the range of [4, 17] m/s. In addition, as HSCAT-C is a rotating pencil-beam scatterometer, some common error characteristics are also clearly found in the wind products. Based on the validation results and existing scatterometer application experience, it is believed that the availability of the HSCAT-C wind product would greatly benefit the scientific and user community.

Atmospheric turbulence and land - atmosphere energy transfer characteristics in the surface layer of the Northern slope of Mt. Qomolangma area : abstract

Based on the turbulent data collected at Quzong site, on the northern slope of Mt. Qomolangma, from April 2005 to March 2006, macro-scale turbulent statistical characteristics and land-atmosphere energy transfer before and after the onset of southwest monsoon were acquired by the eddy correlation method. It was found that Monin-Obukhov similarity theory is applicable for Mt. Qomolangma area. The relationship between normalized wind speed standard deviation and atmospheric stability, variances of normalized temperature and humidity standard deviation with atmospheric stability were simulated in the study. It was also found that energy balance components (net radiation flux, sensible heat flux, latent heat flux and soil heat flux) and surface heating filed had evident diurnal and seasonal changes. Especially under the influence of southwest monsoon, the sensible heat flux and latent heat flux in Quzong area have evident opposite changing trends. The variation characteristics of other surface parameters (surface reflectance and Bowen ratio) is very clear before and after the breakout of southwest monsoon.

Estimating Weibull Parameters for Wind Energy Applications using Seven Numerical Methods: Case studies of three costal sites in West Africa

In this study, the effectiveness of seven numerical methods is evaluated to determine the shape (K) and scale (C) parameters of Weibull distribution function for the purpose of calculating the wind speed characteristics and wind power density. The selected methods are graphical method (GPM), empirical method of Justus (EMJ), empirical method of Lysen (EML), energy pattern factor method (EPFM), maximum likelihood method (MLM) moment method (MOM) and the proposed. Hybrid method (HM) derived from EPFM and EMJ. The purpose is to identify the most appropriate method for computing the mean wind speed, wind speed standard deviation and wind power density for different costal locations in West Africa. Three costal sites (Lomé, Accra and Cotonou) are selected. The input data was collected, from January 2004 to December 2015 for Lomé site, from January 2009 to December 2015 for Accra site and from January 2009 to December 2012 for Cotonou. The results indicate that the precision of the computed mean wind speed, wind speed standard deviation and wind power density values change when different parameters estimation methods are used. Five of them which are EMJ, EML, EPF, MOM, ML, and HM method present very good accuracy while GPM shows weak ability for all three sites.