Wind Speed Estimation Research Articles

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

A numerical study of the effect of building height and shape on amount of wind energy available on the roof

With the aid of renewable energy, carbon footprints can definitely be reduced. Wind energy is such a renewable energy source because it is not depleted when used. Wind energy utilization through a wind turbine and its outstanding conversion efficiency makes any building construction economic. Building height and contour of its roof shape plays an important role in the design of wind turbine placement. In this research, a numerical study was conducted to estimate wind speed, the intensity of turbulence, and wind-specific energy available on the surface of several different shapes and heights of buildings designed using the ANSYS program. The main objective is to choose the appropriate location for installing a wind turbine on the roof of the building to convert wind energy into electrical energy. In order to ensure the objective, numerical modeling of the reference building along with the wind effect on its roof was validated with experimental reference values. ANSYS software was used for numerical modeling of the building with different shapes of the roofs along with different heights respectively and comparing the results of the cases studied on the surface of the building, an optimal design shape was chosen that gives the largest amount of wind that can be harvested, by adopting the value of the highest speed and the intensity of the minimum turbulence as a measure of comparison.

The Role of Phytoplankton Biomacromolecules in Controlling Ocean Surface Roughness

Satellite altimetric data routinely map sea surface topography by measuring the ocean return signal. One source of altimeter measurement contamination occurs when the radar ocean backscatter becomes unusually large, a situation termed a Sigma-0 bloom. Past research suggests Sigma-0 blooms are associated with weak wind and natural surface slick conditions where capillary waves at the air–sea interface are suppressed. To date, no explicit connection between these conditions and Sigma-0 bloom presence has been provided. Using a series of simplified equations, our reduced model determines capillary wave heights from estimates of planktonic carbon concentrations and regional wind speed. Our results suggest that the radar signal reflection increases as capillary wave height decreases. This relationship depends on surfactant concentration, surfactant composition, and wind speed. Model sensitivity analysis indicates that the interface reflectivity depends on biological activity and wind speed. Our proposed simplified model provides a method to identify potential Sigma-0 bloom regions. We conclude that because of the demonstrated impact of biological surfactants on ocean roughness, it is necessary to consider the biological activity, i.e., phytoplankton bloom events, when interpreting signals from radar altimetry and when developing ocean hydrology models.

Modelling the Wind Turbine by Using the Tip-Speed Ratio for Estimation and Control

The development of dynamic models for control purposes is characterised by the challenge of finding a compromise between the minimum necessary information about the system dynamics contained in the model and a model with a low level of complexity such that the model-based control system design becomes comfortable. To achieve this balance, a modified dynamic model for the drivetrain of a wind turbine is proposed in this contribution. The main idea is to introduce the tip-speed ratio as a state variable so that an interval observer can be designed in such a way that its estimates can be used in the torque control during the partial load operation as well as for the estimation of the effective wind speed. During the runtime, the observer’s matrix gain is recalculated to adapt the behaviour to the current operational state, which changes all the time with the wind speed. Besides the theoretical formulation, a numerical example of a 20 MW reference wind turbine illustrates the utility of the method. The results show good control performance concerning the tip-speed ratio control loop and a satisfactory estimation of the effective wind speed.

A conditional approach for joint estimation of wind speed and direction under future climates

Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.

Use of the Halphen distribution family for mean wind speed estimation with application to Eastern Canada

We introduce the family of three-parameter heavy-tailed distributions, the Halphen distribution family (HDF), to model the mean wind speed for the purpose of wind energy estimation. The HDF has a number of properties favorable to model wind speed data, such as lower bound at zero (absence of location parameter), flexibility to cover a large range of shapes, and an explicit form of moment generating functions. We examined 126 stations in Eastern Canada (125 stations were heavy-tailed with positive excess kurtosis) and found that HDF provides fit superior to the most commonly used distribution for this purpose, the two-parameter Weibull distribution, in 100% of the stations according to the Akaike information criterion (AIC). HDF was compared against 4 two-parameter models (Gaussian, Weibull, Gamma, and inverse Gamma) and 3 three-parameter models (generalized extreme value, generalized Gamma and Burr). The most common best-fit distribution for the stations in the Eastern Canada case study is the HDF (46% according to AIC). The results of the case study show that wind observations cannot be fit by the Halphen inverse type B, but can be modelled by Halphen A and especially well by Halphen B (minimum of Kolmogorov-Smirnov statistic among candidate distributions). 87 stations exhibited class D tail behavior. No correlation between tail behavior class and best-fit distribution was observed. We encourage the use of Halphen A and Halphen B as candidate distributions for wind resource estimation studies that are based on mean wind speed estimation.

High-Gain Observer-Based Advanced Nonlinear Control of a Grid-Connected Wind Energy Conversion System with Sensorless Maximum Power Point Tracking

This paper deals with the control development of a wind energy conversion system (WECS) interfaced to a utility grid by using a doubly fed induction generator (DFIG), a back-to-back (B2B) converter and an RL filter for optimal power extraction. The aim was to design a sensorless controller to improve the system reliability and to simultaneously achieve the regulation of the generator speed, reactive power and DC-link voltage. The proposed global control scheme combines: (i) a high-gain observer employed to estimate the generator speed and the mechanical torque, usually regarded as accessible, (ii) a sensorless MPPT block developed to provide optimal generator speed reference, which is designed on the basis of the mechanical observer and a polynomial wind-speed estimator and (iii) a finite-time controller (FTC) applied to the B2B converter to meet the output reference’s tracking objectives in a short predefined finite time by using the backstepping and Lyapunov approaches. The proposed controller performance is formally analysed, and its capabilities are verified by numerical simulations using a 2 MW DFIG wind turbine (WT) under different operating conditions.

A one-year comparison of new wind atlases over the North Sea

The New European Wind Atlas (NEWA) and the Norwegian hindcast archive (NORA3) database have become publicly available since the end of 2019 and mid-2021, respectively. They aim to model the long-term wind climatology with a spatial resolution of ca. 3 km and a temporal resolution of 1 h (NORA3) or 30 min (NEWA). Both products have a high potential for wind energy applications. Although their geographical coverages partly overlap, an inter-comparison of the NEWA and NORA3 databases in an offshore environment is still lacking. The paper compares the hourly mean wind speed and wind direction recorded in 2009 at the FINO1 platform (North Sea) with hindcast data from the NEWA and the NORA3 database. Both products were found to provide reliable estimates of the mean wind speed at 101 m above sea level. However, NORA3 shows slightly better performances than NEWA for the mean wind speed in terms of root-mean-square error, bias, earth mover’s distance (EMD) and Pearson correlation coefficient. For the mean wind direction, a larger circular EMD than previously documented is found, which could be due to a directional bias in the wind vane data. Finally, the Brunt-Väisälä frequency is computed using sea-surface temperature analyses and the air temperature from NORA3 and NEWA at 101 m above sea level. The encouraging description of the static atmospheric stability by the wind atlases opens the possibility to study in more detail thermally-induced wind events for wind resource assessment or wind turbine design.

Discrimination of Oceanic Whitecaps Derived by Sea Surface Wind Using Sentinel‐2 MSI Images

AbstractOceanic whitecaps, as a manifestation of the wind‐wave breaking process and the medium of air‐sea exchange, play an important role in sea surface research. The observational data of oceanic whitecap coverage are generally in situ photographs or videos. The use of spaceborne optical sensors to capture oceanic whitecaps could provide more spatial distribution and dynamic marine environment information. In this study, more than 100 Sentinel‐2 Multi‐Spectral Instrument (MSI) images and corresponding synchronous buoy wind speed data were collected to investigate oceanic whitecaps. A curvature image processing method was employed to successfully identify oceanic whitecaps in MSI images and the whitecap coverage was estimated based on the whitecap recognition results. We established a power‐law model of MSI‐estimated whitecap coverage (W) and whitecap number density with buoy‐measured wind speed at a height of 10 m above the sea surface (U10). In addition, the differences between the MSI‐derived model and in situ measurement‐derived models which are mainly caused by the scale effect of optical remote sensing, are discussed. The whitecap‐wind model demonstrates that MSI images can improve the quantification of sea surface wind speed with high spatial resolution. MSI images were used to estimate regional high‐spatial resolution (4 km) wind speeds, and the merged results (0.25°) were verified with the hourly wind products of the fifth generation European ECMWF (Centre for Medium‐Range Weather Forecasts) atmospheric reanalysis (ERA5). Moreover, the influence of sunglint on whitecap recognition and whitecap coverage was also analyzed. The reflectance of whitecap‐affected pixels in MSI images increased obviously with wind speed compared with background seawater in weak sunglint reflection area. These results indicated that the parameters of oceanic whitecaps derived from high‐spatial resolution optical images can be used to estimate wind speed and provide a new approach for monitoring marine dynamic environments in the future.

Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous.

Application of Microwave Space-Based Environmental Monitoring (SBEM) Data for Operational Tropical Cyclone Intensity Estimation at the Joint Typhoon Warning Center

Abstract The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) radiometers, and synthetic aperture radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the poststorm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the poststorm BT process, and provide examples of how these data influenced forecaster decision-making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new datasets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data-sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.

Interagency discrepancies in tropical cyclone intensity estimates over the western North Pacific in recent years

AbstractThis study investigates interagency discrepancies among best‐track estimates of tropical cyclone (TC) intensity in the western North Pacific, provided by the Joint Typhoon Warning Center (JTWC), the China Meteorological Administration (CMA), and the Regional Specialized Meteorological Center (RSMC) Tokyo during 2013 to 2019. The results reveal evident differences in maximum wind speed (MSW) estimates, where linear systematic differences are significant. However, the Dvorak parameter (CI) numbers derived from the MSWs reported by the three agencies are internally consistent. Further analysis suggests that the remained CI discrepancies are related to differences in the estimation of intensity trends, initial intensities, and TC positions among these datasets. In addition, the CI estimates provided by the JTWC for TCs over the open ocean are generally higher than those reported by the CMA and RSMC. However, the estimates from CMA and RSMC tend to give higher TC intensities for the TCs in the mainland and coastal areas of China and Japan, respectively, than those over the open ocean with the same intensity in JTWC dataset. This pattern potentially reflects the extensive use of surface observations by these two agencies for landfalling and offshore TCs. These results may help the research community to get more accurate details about the TCs in WNP from the best track datasets of different agencies.

FORECASTING WEIBULL PARAMETERS WITH A NOVEL ALTERNATIVE GRAPHICAL TECHNIQUE FOR LOW WIND SPEEDS

This paper analyses wind speed estimation for Weibull distribution using various methods. According to a previous study, the existing methods primarily target areas with moderate to high-velocity rates, and Malaysia is a tropical country with pleasant breezes all year. As a result, this research aims to devise the most efficient method of forecasting wind speeds in low-speed areas. The researcher compared existing methods such as the Moment of Method, Empirical Method, Graphical Method, Maximum Likelihood Method and the newly proposed Alternative Graphical method. The finding indicates that the novel proposed approach, the Alternative Graphical Method, is superior regarding Goodness of Fit, Kolmogorov Smirnov and Chi-Square. For Kolmogorov Smirnov, the Alternative Graphical Method is 3.4 % better than the second-best method. At the same time, the usage of Chi-Square is again at a top position, with a 61 % disparity between it and the second and third best places. However, the Alternative Graphical Method is in second place for Anderson Darling, but the forecast performance with a minimum difference of 0.3 %. These findings imply that the Alternative Graphical Method capable of making more precise predictions than current methods.

Quantification of motion-induced measurement error on floating lidar systems

Abstract. Floating lidar systems (FLSs) are widely used for offshore wind site assessment, and their measurements show good agreement when compared to trusted reference sources. However, some influence of motion on mean wind speed data from FLS has to be assumed but could not have been quantified with experimental methods yet because the involved uncertainties are larger than the expected impact of motion. This study describes the motion-induced bias on horizontal mean wind speed estimates from FLS with the help of simulations of the lidar sampling pattern of a continuous-wave (CW) velocity–azimuth display (VAD) scanning wind lidar. Analytic modeling is used to validate the simulations. It is found that the mean bias depends on amplitude and frequency of motion, the angle between motion and wind direction, and wind speed and strength of wind shear. The simulations are used to quantify the measurement deviation that is caused by motion for the example of the Fugro SEAWATCH Wind LiDAR Buoy (SWLB) carrying a ZX 300M profiling wind lidar. The strongest bias of −0.67 % of the measurement values was estimated for a test case with “strong” waves aligned with the inflow wind direction. Under “normal” wave conditions the bias is smaller. The reason for these low errors lies in a fortunate combination of the frequencies of lidar prism rotation and tilt motion.

A transfer method to estimate hub-height wind speed from 10 meters wind speed based on machine learning

The estimation of hub-height wind speed is critical to a comprehensive wind resource assessment, particularly for the evaluation of future energy mix scenarios. However, gridded datasets of wind speeds are often limited to near-surface winds, especially when it comes to climate model projections, which is a real limitation for using climate models. This study develops a transfer method to calculate 100 m wind speed using three machine learning methods, including the Least Absolute Shrinkage Selector Operator, Random Forest (RF) and extreme Gradient Boost (XGBoost). Compared with the traditional algorithm, based on empirical formulae, the tested machine learning-based algorithms allow much more accurate estimates of 100 m wind speeds. RF and XGBoost have good performance on the hourly scale, and correct the major biases of the classical, simplified algorithms, especially in the diurnal cycle of hub-height wind speeds. RF appears to be the best algorithm when compared with the reanalysis data. In addition, the machine learning transfer model is applied to 19 regional climate projections. Results show that the 100 m wind speed has decreased in most of Europe during 1979–2019, which is consistent with the observed stilling of surface winds in recent years. This trend is projected to increase in the future, under an uncurbed greenhouse gas emission scenario, which indicates adverse effects for the development of wind power generation in Europe. The approach established in this study can be applied to obtain numerical climate model outputs accurately, which is critical to the estimation of the long-term changes of global renewable energy resources.

Estimation of wind speed distribution with time window and new kernel function

Wind speed distribution analysis is important for selecting the potential wind farm and improving wind energy utilization efficiency. However, due to the randomness, intermittence, and chaos of wind speed, it is difficult to accurately estimate its probability distribution. Many studies use specific probability density function to estimate wind speed distribution, but only a few analyze the changes of wind speed distribution in the same region. The purpose of this study is to improve the accuracy of wind speed distribution estimation and analyze the changes in wind speed distribution under different time windows. Three new kernel functions are proposed to improve the accuracy of distribution estimation. A new point-to-point comparison method is proposed to evaluate the goodness-of-fit of wind distribution. A new time window analysis method is applied to analyze the monthly, quarterly, and semiannual wind speed distribution. The results show that (a) under different time windows, the wind speed distribution in Hexi Corridor is different; (b) the performance of kernel function is affected by its peak value and shape; and (c) one of the three new kernel functions has the smallest error, in which mean square errors in monthly, quarterly, and semiannual time windows are 0.0057, 0.0061, and 0.0056, respectively.

Offshore wind resource assessment off the coast of Daejeong, Jeju Island using 30-year wind estimates

Long-term offshore wind resource assessment was performed for the offshore site off the Daejeong coast on Jeju Island of South Korea. A jacket type offshore met mast was installed at the site to measure wind and meteorological conditions for 1 year from 1 July 2015 to 30 June 2016. The wind conditions were measured at 32.5 m, 72.5 m and 100.5 m above mean sea level (AMSL). Fundamental wind characteristics such as average wind speed, wind direction frequency, turbulence intensity and so on were analyzed utilizing 1-year wind measurements. In order to correct the limited period of 1-year measurements to long-term conditions, the Measure-Correlate-Predict (MCP) method was applied to estimate 30-year wind conditions using nearby Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. Also, wind turbine classes suitable for the offshore site were determined based on extreme wind speed estimations and the measured ambient turbulence intensity. Five commercial wind turbines were chosen for estimation of the 30-year annual energy productions (AEPs) and capacity factors (CFs). As a result, it was estimated that the 30-year average wind speed was 7.86 m/s at 100.5 m AMSL. The wind turbine class II rating was estimated to be suitable for enduring extreme wind speed for a return period of 50 years. For the 30-year results, the minimum CF of 30.48% was estimated, while the maximum nearly reached 45%.

Support Vector Regression Approach for Wind Forecasting

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.

Support Vector Regression Approach for Wind Forecasting

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.

Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.