Wind Speed Estimation Research Articles

Thunderstorm impact on the built environment: A full-scale measurement and post-event damage survey case study

This paper investigates the impact of an intense thunderstorm on the urban area of Sânnicolau Mare, Romania, based on the meteorological data collected by full-scale field measurement and the damage data of low-rise buildings obtained by post-event survey. The meteorological data throughout the entire thunderstorm process were collected from two sources, namely, a wind and structural monitoring system of a 50 m-tall lattice tower and a state-operated meteorological station, both located at the outskirts of the city. The damage data were collected by post-event in-situ survey with crowdsourcing techniques, providing information of both wind- and hail-induced damage to over 200 low-rise buildings. Based on the meteorological and damage data, the damage mechanisms were revealed by conducting forensic engineering analysis for two representative buildings which respectively sustained severe wind- and hail-induced damage. Moreover, the widely used estimation scales for wind intensities, such as the Fujita scale, were validated by anemometric records and damage observations. Finally, the feasibility of utilizing the hail-induced damage to building facades as a new indicator for the estimation of wind speed and direction was discussed. This study aims to further understand the impact of thunderstorms on low-rise buildings, so as to deliver essential information for their storm-resistant designs.

Utilizing ASCE/SEI 7 to Estimate Wind Speeds for Forensic Investigations

The American Society of Civil Engineers/Structural Engineering Institute (ASCE/SEI) 7 standard is utilized to determine design wind loading on buildings and other structures. However, it can also be utilized in a forensic capacity to approximate a wind speed that would cause specific conditions to occur, such as the overturning of a structure. This paper provides a brief overview of the ASCE/SEI 7 method for wind loading and discusses the use of various adjustment factors used to determine the wind on structures, including wind directionality factor, velocity pressure coefficient, topographic factor, and the ground elevation factor. A clear understanding of these factors — and how to apply them — is crucial to estimating a wind speed and resulting force to cause a particular event or condition to occur.

Copula-based estimation of directional extreme wind speeds: Application for wind-resistant structural design

Accurate probabilistic modelling of wind loads has important implications to the wind-resistant design of structures. Whereas, most previous studies merely concerned the statistical analysis of wind speed, while the effect of wind direction was usually neglected in the wind load modelling. This paper thereby proposes a novel copula-based approach to construct the joint distribution of wind speed and direction for determining the directional extreme wind speed. Based on the wind records at six typical sites in Hong Kong, a series of parametric and nonparametric copula models are applied to model the joint probability density function (JPDF) of wind speed and direction. The goodness-of-fit test indicates that the Bernstein copula is the best model to describe the correlation between variables. Furthermore, by applying the superior copula model, directional extreme wind speeds with a 10-year return period at the six selected sites are determined on basis of the conditional probability theory. The results show that the estimated extreme wind speeds vary obviously among different directions. And, the estimated directional extreme wind speed may be unreliable in some directions when ignoring the joint effect of wind speed and direction in wind load modelling. Notably, the presented method is an effective and practical tool to estimate the directional extreme wind speed with a certain return period. This paper aims to provide useful reference for the wind-resistant design in tropical cyclone-prone regions.

Estimation of wind speed by artificial intelligence method: A case study

Estimation of wind speed by artificial intelligence method: A case study

Research on wind turbine variable pitch control based on wind speed estimation

With the continuous promotion of the “double carbon” strategy, the capacity of wind turbines is increasing, and the dimensions of key parts such as the diameter of the wind turbine wheel, the height of the tower, and the radius of the tower tube are also increasing. Large wind turbines have a large inertia, so the execution time of the pitch actuator and the response time of the pitch action are usually longer, which leads to large fluctuations in the output power of the wind turbine when the wind speed changes rapidly under the working conditions above the rated wind speed and leads to complex loading problems. Therefore, in this paper, the design of pitch control is carried out by estimating the effective wind speed at the wind turbine surface and using the inverse system method to transform the nonlinear wind turbine system into a linear decoupled form. This method improves the pitch response speed, reduces the output power fluctuation, and better suppresses the load of the turbine to ensure the safe and stable operation of the turbine

Automated Large-Scale Tornado Treefall Detection and Directional Analysis Using Machine Learning

Abstract In many regions of the world, tornadoes travel through forested areas with low population densities, making downed trees the only observable damage indicator. Current methods in the EF scale for analyzing tree damage may not reflect the true intensity of some tornadoes. However, new methods have been developed that use the number of trees downed or treefall directions from high-resolution aerial imagery to provide an estimate of maximum wind speed. Treefall Identification and Direction Analysis (TrIDA) maps are used to identify areas of treefall damage and treefall directions along the damage path. Currently, TrIDA maps are generated manually, but this is labor-intensive, often taking several days or weeks. To solve this, this paper describes a machine learning– and image-processing-based model that automatically extracts fallen trees from large-scale aerial imagery, assesses their fall directions, and produces an area-averaged treefall vector map with minimal initial human interaction. The automated model achieves a median tree direction difference of 13.3° when compared to the manual tree directions from the Alonsa, Manitoba, tornado, demonstrating the viability of the automated model compared to manual assessment. Overall, the automated production of treefall vector maps from large-scale aerial imagery significantly speeds up and reduces the labor required to create a Treefall Identification and Direction Analysis map from a matter of days or weeks to a matter of hours. Significance Statement The automation of treefall detection and direction is significant to the analyses of tornado paths and intensities. Previously, it would have taken a researcher multiple days to weeks to manually count and assess the directions of fallen trees in large-scale aerial photography of tornado damage. Through automation, analysis takes a matter of hours, with minimal initial human interaction. Tornado researchers will be able to use this automated process to help analyze and assess tornadoes and their enhanced Fujita–scale rating around the world.

Usage of Statistical Techniques to Monitor the Performance of Wind Turbines

Calculating the yearly energy output, which keeps the balance between both the generation and consumption of electricity, is made easier with the use of wind energy production estimates for grid interfaces. Effective wind speed forecasting is crucial for achieving this goal. In this research, linear statistical models of prediction Generalized Autoregressive Score (GAS), GAS model with exogenous variable x (GASX), and Autoregressive Integrated Moving Average (ARIMA) are the models used to estimate wind speed accurately. Additionally, the modeling of non-linear time-series data has been done using the Non-Linear GASX (NLGASX) statistical predictive modeling method. Additionally, the REctified Linear Unit (RELU), Softmax, Hyperbolic Tangent (TANH), and Sigmoid modeling approaches are used to optimize the suggested NLGASX model. In comparison to existing models, the suggested optimized NLGASX approach performs significantly better. In order to anticipate wind power, the wind power curve modeling additionally takes wind speed as an input. The estimated wind power has been used to determine the yearly energy

Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.

Component-based hurricane vulnerability model for mid/high-rise commercial residential buildings

This paper focuses on the vulnerability of residential mid/high-rise buildings (MHRB) to hurricane-induced wind and wind-driven rain ingress, with a special emphasis on interior and contents damage. A component-based methodology utilizes the physics of rainwater ingress, distribution and propagation to provide the basis for the proposed MHRB risk assessment model. The model combines estimates of wind speed, impinging rain and surface run-off wind driven rain, envelope defects and breaches, interior water distribution and propagation, and component cost analyses to project envelope, interior and contents damage. The paper describes the model, verifies that it satisfies logical relationships to risk, and compares it against existing MHRB models. The new physics-based interior and contents vulnerability model should facilitate the evaluation of the cost effectiveness of mitigation measures.

Improved tropical cyclone wind speed estimation for microwave altimeter using machine learning

Satellite altimeters can provide excellent global wind speed at 10 m above the sea surface (U10), however, the U10 becomes inaccurate and difficult to measure in tropical cyclone conditions. The violent wind, rough waves and torrential rain manifest an exceptionally complex ocean-atmospheric environment for wind estimation. Although the backscatter signal is measured equally well in normal condition, the interpretation is not straightforward in tropical cyclones that requires complex associations with ocean-atmospheric geophysical variables. Typical U10 regression model developed in normal atmospheric conditions would inevitably reduce the estimation quality and encounter high modelling uncertainties from high dimensional input data that provide ill-posed solutions in extreme U10 estimation. However, other secondary parameters simultaneously measured by the altimeter have properties that convey additional atmospheric information to enhance U10 estimation accuracy in storm condition. Therefore, the present study proposes machine learning (ML) approaches based on artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) to integrate the multi-dimensional parameters and provide accurate U10 estimates for different parameter combination. Results suggest that the GPR method, considering atmospheric and sea surface related parameters, can provide the highest accuracy of U10 up to 35 ms−1 with quality perseverance against rain contamination. This study highlights the quality improvement of altimeter U10 estimation and the assessment method of ML in dealing with complex ocean-atmospheric interactions.

Enhancing the accuracy of wind speed estimation model using an efficient hybrid deep learning algorithm

Estimation of wind speed holds great significance for efficient operation of wind farms and grid stability. Randomness of wind speed poses a great challenge for accurate estimation of wind speed. Hence for addressing this problem, a convolutional neural network (CNN) and long short-term memory (LSTM) based novel hybrid deep learning (DL) short-term is proposed. The prediction model is built using pre-processed wind speed dataset from Jaisalmer district, Rajasthan. For designing the proposed model, the samples of the dataset are bifurcated into four seasons such as autumn, summer, winter and spring and the prediction performance are evaluated for each season separately. The proposed hybrid algorithm (CNN-LSTM) is implemented and a comparison is made with different DL algorithms. The predictive performance are calculated by observing the statistical metrics such as root mean squared error (RMSE), mean absolute error (MAE), relative RMSE, normalized RMSE and coefficient of determination (R2). It is observed that the value of statistical metrics such as MAE, RMSE, relative RMSE and normalized RMSE of the proposed algorithm are lower than the other DL models for all the four seasons. The higher value of R2 obtained by the CNN-LSTM indicates the model is best fitted with the dataset. Conclusively, the proposed model demonstrates improved adaptability and higher prediction accuracy in comparison to the predictive skills of models utilizing the same dataset.

Wind as a natural hazard in Poland

Abstract. The paper deals with wind speeds of extreme wind events in Poland and the descriptions of their effects. Two recent estimations developed by the Institute of Meteorology and Water Management in Warsaw and by Halina Lorenc (a previous co-worker of the institute) are presented here. Their strong and weak points are briefly described. The 37 annual maximum gusts of wind speeds measured at all meteorological stations between 1971 and 2007 are analysed by an extremal probability paper, block-maxima (BM), and peak-over-threshold (OVT) approaches. Based on the measured and estimated wind speeds (taken from existing literature), the authors suggest new estimations for extreme winds that may occur in Poland. Shortly, Poland will construct important structures, such as a central air terminal and (some) nuclear power plants, so knowledge about extreme winds in our country is significant for engineers who will design these structures.

Feasibility of eolian sound for urban wind speed estimation

AbstractWind‐speed measurement is crucial in designing wind‐resistant buildings and controlling the wind environment in urban areas. However, the current methods of measuring wind speed, such as anemometers and Doppler lidar, are associated with high costs. This study investigated an alternative approach to measuring wind speed using the eolian sound emitted from cylindrical objects in urban settings, such as electrical cables. The proposed method relies on a relationship between the frequency of Karman vortex generation, which gives rise to eolian sound, and wind speed, considering the diameter of a cylindrical object. This study addressed 2 research questions: (1) the possibility of capturing and identifying the eolian sound emitted from a cylindrical object using a generic recording device and whether the peak frequency of the recorded sound aligns with the theoretically predicted frequency; (2) the feasibility of estimating the direction of the eolian sound source to identify the object responsible for the sound emission. The authors conducted a series of experiments involving artificially generated eolian sound in an outdoor environment and analyzed the recorded sound signals. The analysis results were encouraging, confirming positive responses to both research questions. These results indicate the potential for a novel method capable of estimating wind speed.

Computation harvesting from nature dynamics for predicting wind speed and direction.

Natural phenomena generate complex dynamics because of nonlinear interactions among their components. The dynamics can be exploited as a kind of computational resource. For example, in the framework of natural computation, various natural phenomena such as quantum mechanics and cellular dynamics are used to realize general purpose calculations or logical operations. In recent years, simple collection of such nature dynamics has become possible in a sensor-rich society. For example, images of plant movement that have been captured indirectly by a surveillance camera can be regarded as sensor outputs reflecting the state of the wind striking the plant. Herein, based on ideas of physical reservoir computing, we present a methodology for wind speed and direction estimation from naturally occurring sensors in movies. Then we demonstrate its effectiveness through experimentation. Specifically using the proposed methodology, we investigate the computational capability of the nature dynamics, revealing its high robustness and generalization performance for computation.

A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models

In 2022, wind generation accounted for ~10% of total electricity generation in the United States. As wind energy accounts for a greater portion of total energy, understanding geographic and temporal variation in wind generation is key to many planning, operational, and research questions. However, in-situ observations of wind speed are expensive to make and rarely shared publicly. Meteorological models are commonly used to estimate wind speeds, but vary in quality and are often challenging to access and interpret. The Plant-Level US multi-model WIND and generation (PLUSWIND) data repository helps to address these challenges. PLUSWIND provides wind speeds and estimated generation on an hourly basis at almost all wind plants across the contiguous United States from 2018–2021. The repository contains wind speeds and generation based on three different meteorological models: ERA5, MERRA2, and HRRR. Data are publicly accessible in simple csv files. Modeled generation is compared to regional and plant records, which highlights model biases and errors and how they differ by model, across regions, and across time frames.

Comparative analysis of different methods in estimating wind speed distribution, and evaluation of large‐scale wind turbine performance in Rahva‐Bitlis, Turkey

AbstractIn this study, wind characteristics and electricity generation potential from wind energy were investigated in the Bitlis‐Rahva region in eastern Turkey. Ten‐minute wind data from the Bitlis meteorological station were used in the study. The determination of the wind speed (WS) distribution was carried out using the WindPRO program together with the Weibull and Rayleigh distributions. R2, RMSE, and χ2 goodness‐of‐fit tests were used to measure the accuracy of the velocity distribution process. A digital elevation model of the power generation site was created to evaluate wind data and predict turbine performance. Wind power generation performance was evaluated using five different wind turbines (WTs). A power curve model was developed for the WTs to determine the capacity for energy generation. According to the error tests, it has been determined that the results obtained with the WindPRO program can represent the observed wind data most accurately. The results from the WindPRO program analysis showed an average annual WS of 3.26 m/s and a power density of 49.77 W/m2. Increasing height was found to increase WS and power density. The prevailing wind direction was found to be South‐Southwest with a frequency value of 27.5%. The largest capacity factor was obtained from the WT with the largest rotor diameter and rated power. Out of all the WTs examined, the turbine with the largest rated power and rotor diameter produced the greatest amount of energy. According to the capacity factor values calculated for the selected WTs, the region is not considered efficient for wind energy investments. However, as WS and power density increase with increasing altitude, it is thought that different higher parts of the region may be more efficient in terms of wind energy utilization.

Proxy Observations of Surface Wind from a Globally Distributed Network of Wave Buoys

Abstract In the equilibrium range of the wave spectrum’s high-frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021 to 2022) when compared with collocated observations derived from satellites (yielding over 20 000 collocations) and reanalysis data. We consider physics-motivated parameterizations (based on frequency−4 universal tail assumption), inverse modeling (estimate wind speed from spectral energy balance), and a data-driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test data reveals comparable performance across methods with generally of order 1 m s−1 root-mean-square difference with satellite observations.

An image processing approach to reconstruct wind using long-range wind lidars

Lidar-based wind sensing technology, originally used in the wind energy sector, is now being utilized in wind engineering to monitor wind action for designing fjord-crossing infrastructure like long-span bridges. Accurate estimation of design wind loads is crucial for the design process of such bridges. This paper examines wind data from two pairs of long-range lidars positioned along one side of the Sulafjorden, Norway, in a measurement campaign initiated by the Norwegian Public Roads Administration (NPRA). Two different scanning modes, the Plan Position Indicator (PPI) mode and the staring mode, with the fixed line-of-sight (LOS) orientation, are used to determine the line-of-sight wind speeds, the wind direction, and the related along wind speed. An image processing approach is used to compute along wind velocity information from all range gates based on the LOS wind speeds from two near-parallel lidar beams. The estimated along wind velocity is validated through wind data calculated at the intersection point of the two lidars, which provides velocity data with two horizontal components. The image-based reconstruction is found to produce reasonable wind speed estimates with a goodness-of-fit coefficient of R2 = 0.903. The mean wind direction estimate is smoothened but still comparable to the actual wind direction. The image processing approach shows promising potential to provide wind speed and direction information for all range gates for the lidar setup used, which can supplement traditional single-point wind velocity characterization by dual lidars.

Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies

A main external factor that limits the energy assessment of high-rise buildings in urban areas is the vertical wind conditions. Conventional buildings' energy assessment tools apply a universal vertical wind profile estimation equation in all directions and only consider four typical terrain types. However, the various shape and configurations of buildings in cities can induce the unique wind speed in each direction, and the vertical wind speeds in the same terrain type can be different even in the same city. To get more realistic vertical wind conditions, this study developed an omnidirectional urban vertical wind speed estimation method with direction-dependent building morphologies. A pie-shaped segment method is applied to calculate building morphological features in approaching wind direction. Machine learning methods are developed to calculate parameters of the modified direction-dependent power law urban wind function to convert the Typical Meteorological Year (TMY) wind speed to urban vertical wind speeds. To validate the proposed method, wind tunnel data in Hong Kong are collected for a case study. The results indicate that the proposed method with morphological inputs can effectively estimate the vertical wind speed at different wind directions and heights, and deep neural networks and support vector regression have better performance.

Analysis and multi-objective optimisation of wind turbine torque control strategies

Abstract. The combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking wind turbine control scheme has seen recent and increased traction from the wind industry. The modern control scheme provides a flexible trade-off between power and load objectives. On the other hand, the Kω2 controller is often used based on its simplicity and steady-state optimality and is taken as a baseline here. This paper investigates the potential benefits of the WSE–TSR tracking controller compared to the baseline by analysis through a frequency-domain framework and by optimal calibration through a systematic procedure. A multi-objective optimisation problem is formulated for calibration with the conflicting objectives of power maximisation and torque fluctuation minimisation. The optimisation problem is solved by approximating the Pareto front based on the set of optimal solutions found by an explorative search. The Pareto fronts were obtained by mid-fidelity simulations with the National Renewable Energy Laboratory (NREL) 5 MW turbine under turbulent wind conditions for calibration of the baseline and for increasing fidelities of the WSE–TSR tracking controller. Optimisation results show that the WSE–TSR tracking controller does not provide further benefits in energy capture compared to the baseline Kω2 controller. There is, however, a trade-off in torque control variance and power capture with control bandwidth. By lowering the bandwidth at the expense of generated power of 2 %, the torque actuation effort reduces by 80 % with respect to the optimal calibration corresponding to the highest control bandwidth.