Measured Wind Data Research Articles

Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.

Evaluation of MCP Correlation Algorithms Applied to Wind Data Series

This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years.

Defining an exposure index along the Schleswig-Holstein Baltic Sea coast

A wind exposure index (EI), which indicates the main physical driver of a coastal system, was developed along the Schleswig-Holstein Baltic Sea (SH) coast – Germany, to demonstrate the highly dynamic coastal stretches (i.e., potential erosion hotspots). The approach used three steps to define more accurate EIs. Initially, a representative wind year (RWY), which has similar physical characteristics as in the long-term data, was defined by analysing measured wind data from 2000 to 2019 at four stations distributed in the entire area of interest. The RWY was identified by a statistical comparison of wind speeds in 5 classes and 36 directional sectors between summer to summer yearly wind and the overall data. The selected RWY spanned from 01.09.2016 to 31.08.2017 and showed a reasonable agreement with the overall data (Skill = 0.77 and rmsd = 0.56 m/s). Next, high spatiotemporal nearshore hydrodynamics over the RWY were predicted using a model nesting approach of two domains in Delft3D. The predicted nearshore hydrodynamics indicated fair agreements with the measured data (R2: 0.87–0.90 for water levels and 0.75–0.86 for wave heights). Finally, the predicted water level and wave height time series in the nearshore area (∼ 5 m MSL depth) were used for the analysis of the EI adopting a 2-step procedure capturing short- and long-term correlations as well as seasonal long-range dependencies of the time series. This approach allows to model the clustering behaviour of extreme values of both parameters and provides reasonable EIs along the SH coast. The exposed areas display high EIs (e.g., 1 at the east of Fehmarn), while sheltered areas and bays have low values (e.g., 0 at Eckernförde Bay). The higher the EI the stronger the coastal dynamics and thus strong erosion can be expected. Interestingly, the EI varies considerably even along the exposed coastal stretches with long fetches, which indicates the sensitivity of the EI to the local morphology, which determines the nearshore hydrodynamics. Therefore, a definition of the EI based on nearshore hydrodynamics provides an accurate index of local physical drivers of a coastal system. The developed approach can be adopted to any coast, and provides useful information on the potential erosion areas for the coastal managers.

О влиянии неопределенностей на прогнозы ветрового ресурса и выработки электроэнергии

The annual power generation of a wind farm is one of the most important indicators determining the profitability of a wind power project. The methods used to estimate the annual power generation of a wind farm have to consider uncertainties at all stages of the project life cycle. When developing a financial model of a wind power project, it is required to consider information about uncertainties to reduce the error and increase the reliability of the project. Specialists of various countries are improving the efficiency of wind power plants, studying wind energy resources, as well as assessing the efficiency of their use. However, special studies do not pay due attention to the problem of assessing the impact of various uncertainties on the forecasts of wind resource and power generation. Two methods are proposed to calculate uncertainties. They are a deterministic method based on the assumption of independence of various uncertainties, and a Monte Carlo method that simulates the behavior of a physical system many times. The paper considers the uncertainties to be considered during the design of a wind farm and presents the variation ranges. The authors have presented the plots of power generation with various levels of probability of being reached or exceeded the total uncertainty for three variants. It is shown that considering the various uncertainties allows a power generation forecast to be made with increased accuracy. The results obtained are necessary to develop a wind measurement data processing model that allows us to forecast electricity generation by existing wind power plants with increased accuracy.

Assessment of Wind over Complex Terrain Considering the Effects of Topography, Atmospheric Stability and Turbine Wakes

This study proposes a microscale flow model to estimate mean wind speed, fluctuating wind speed and wind direction over complex terrain considering the effects of topography, atmospheric stability, and turbine wakes. Firstly, the effect of topography is considered using Computational Fluid Dynamics (CFD). Next, a mesoscale model is presented to account for the effect of atmospheric stability. The effect of turbine wakes on the mean and fluctuating wind speeds are then represented by an advanced wake model. The model is validated using the measurement data of a wind farm located in the North of Japan. The measured wind data by Lidar at a reference height are horizontally extrapolated to a nearby met mast hub height and validated by a cup anemometer. Moreover, a novel averaging method is proposed to calculate a directional equivalent Monin–Obukhov length scale to account for the effect of atmospheric stability. Finally, the measured wind data at the reference height are vertically extrapolated and validated at the lidar location. The predicted mean and fluctuating wind speeds show good agreement with the measurements.

Evaluating Offshore Wind as a Catalyst for Trinidad and Tobago’s Petrochemical Sector

This study introduces a comprehensive analysis of offshore wind resource potential in Trinidad and Tobago, leveraging both the Wind Atlas Methodology (WAM) and the numerical wind atlas methodologies to address the region’s sparse wind measurement data. Utilizing atmospheric re-analysis data, specifically the ERA5 dataset, in conjunction with the Weather Research and Forecasting (WRF) model, a generalized wind climates (GWCs) for Trinidad and Tobago was generated. These GWCs, refined with topographical and roughness data, guide the siting of offshore wind farms within the Exclusive Economic Zone (EEZ), considering water depths and proximate onshore terrain influences. The study quantifies the economic feasibility of offshore wind through both a levelized cost of electricity (LCOE) analysis and by evaluating the value of redirected natural gas to the petrochemical industry. The LCOE, though currently higher than subsidized domestic electricity rates, is projected to decrease significantly by 2035. Notably, the value of displaced natural gas for petrochemical production offers substantial economic benefits, with potential payback periods for offshore wind investments well under a decade when considering 2021 methanol and ammonia prices. These findings underscore the strategic significance of offshore wind in Trinidad and Tobago’s energy mix. By transitioning to renewable energy sources, the nation can mitigate reliance on fossil fuels for power generation while optimizing natural gas usage in high-value sectors.

Technical, Economical, and Marketing Analyses of Corotating Dual‐Rotor Wind Turbines

In this study, a comprehensive analysis is done on a dual rotor wind turbine (DRWT) in co‐rotating arrangement. First, a co‐rotating dual rotor is designed and its performance is analyzed by blade element momentum theory simulation. Later, the designed co‐rotating DRWT is manufactured and then tested in the field by mounting the wind turbine on a truck. Running the truck steadily at different speeds while logging the performance parameters of the wind turbine, the power curve of the DRWT is obtained. Then, using the power curve of the co‐rotating DRWT and the actual wind measurement data from Çatalca Istanbul, the annual electricity generation of the designed turbine is calculated. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to single rotor wind turbines (SRWTs), in accordance with the literature. Analyses show that with DRWT, around 22% higher power generation can be achieved. Additionally, the payback period for DRWT was found to be less than 1 year. The marketing strategy for introducing the product emphasizes identifying and targeting early adopters and influencers within the market. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to SRWTs, in accordance with the literature.

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

Viability of Open-Source Wind Resource Assessment Software

As the world shifts towards renewable energy sources, newer and larger projects come to fruition every year. Renewable energy projects require a complex analysis before proceeding with development. Part of this analysis procedure is resource assessment. This research aims to determine the viability of using open-source software for industry applications. This is performed through a comparison of the current industry standard software, WAsP, and Continuum. A site with a wide range of land cover conditions is chosen to see how the different software performs under varying conditions. Multiple different measured wind data inputs are used in the comparison, allowing for a comprehensive study. A focus is made on the software’s accuracy, while also comparing the analytics and outputs. The interface, workflow, input data, outputs, computational cost and additional requirements of each software package are discussed. It is found that Continuum is accurate within 8% when compared to WAsP’s annual energy production and capacity factor outputs. Due to the open-source nature of Continuum, it also lends itself to further customised developments that could be advantageous when performing resource assessments. WAsP is found to have preferential reporting outputs, however. This research highlights the opensource resource assessment process, from input to output. Keywords: Wind resource assessment, open-source software, WAsP, Coninuum, AEP, wind energy software

Wind farm layout optimization in complex terrain based on CFD and IGA-PSO

A novel hybrid method is proposed for wind farm layout optimization in complex terrain. Firstly, an elliptical modeling method is presented with the Witoszynski-shaped transition curve in the computational fluid dynamics (CFD) simulations. Wind resources in complex terrain are estimated by combining CFD simulations with measured wind data, and an inverse distance weighting method is introduced. Then, an improved genetic algorithm (IGA) is presented to optimize the wind farm layout, which can significantly improve efficiency and avoid falling into local optima. Finally, to overcome the grid limitation of IGA, a particle swarm optimization (PSO) method is introduced for further optimization, i.e., IGA-PSO. The proposed method is used to optimize the wind farm layout in the complex terrain of Qianjiang, China. Various wake models and cost models are considered in the optimization, where wake models include Jensen, Gaussian wake model (GWM), double Gaussian wake model (DGWM) and cost models include annual energy production (AEP) and net annual value (NAV). The results show that the proposed method outperforms other three algorithms in providing a more favorable wind farm layout in complex terrain.

An optimum design and economic feasibility analysis of wind farms in Kuwait using different wind generation technologies

Kuwait plans to produce 15 % of its electricity from renewable resources by 2030. This paper aims at designing a 300-MW wind farm in six different sites in Kuwait. The study uses the measured wind data at Kuwait International Airport to predict the wind profile (speed and power density) at the selected sites. To validate the work, two different types of wind generation technologies are used in the design. The first type is Full-Converter Wind Turbine Generators (FCWTGs), and the second type is Doubly Fed Induction Generators (DFIGs). The designed wind farms are simulated using the Wind Atlas Analysis and Application Program (WAsP®) to estimate the Annual Energy Production (AEP) for each type of wind farms in each selected site. The location of every wind turbine in each wind farm (both types) for every selected site is accurately determined while considering needed spacing between rows and columns of turbines. The wake effect for downstream wind turbines is considered and estimated in the design. A comparison between the different wind farms in the six sites using the DIFGs and the FCWTGs generators is carried out. The economic feasibility of the designed wind farms is studied using the estimated Annual Energy Production (AEP) in each one of the designed wind farms.

Experimental study on wake interference characteristics of horizontal-axis wind turbine

To investigate the flow field distribution under the interference of multiple wind turbine wakes, two different types of ground-based LiDARs were used to conduct wind measurement experiments in a wind farm. Taking the wake field distribution characteristics of two wind turbines as an example, combined with wind measurement data, the complex wake interference flow field is classified into three typical interference conditions: non-wake, partial wake and full wake. And the wake development characteristics under the above three wake conditions are quantitatively analyzed. The results show that under the condition of non-wake interference, the wakes are independent of each other, the wake recovery rate is basically the same. Under partial wake and full wake interference conditions, the wake center recovery rate of the downstream wind turbine decreases, and the wake recovery rate drops most obviously under the full wake interference condition.

Assessment Technology and Application of Wind Energy Resources in Complex Mountain Areas at High Altitude

Based on the meteorological stations and wind measurement data of a high-altitude region in Liangshan Prefecture, Sichuan Province, collation, analysis, and evaluation are carried out. The results show that: the prevailing wind direction in the region is stable and the wind energy resources have a certain development value, but the turbulence intensity is high and belongs to IEC Class B, which has a certain impact on the fatigue load of wind turbines, and the safety of local wind turbines needs to be reviewed.

A physics-informed neural network-based approach to reconstruct the tornado vortices from limited observed data

The measured wind data of tornado vortices is often insufficient and low-resolution for analysis of tornado-induced loads and effects on civil structures. This study proposes a physics-informed neural network-based (PINN-based) approach to reconstruct the tornado vortices from limited observed data. First, the PINN model is constructed by embedding the simplified tornado governing equations in a deep neural network as the prior physical information. The PINN model is then validated by the reconstruction of different numerical tornado vortices generated by large eddy simulations. Parametric analysis is also conducted to investigate the influence of the structure of the PINN and the number of observation points. Finally, the proposed approach is applied to reconstruct a real tornado vortex. Results show that the PINN-based method can accurately reconstruct the numerical tornado vortices in different stages with a relative error below 4% for tangential velocity and vertical velocity, and 11% for radial velocity. Increasing the number of observation points and the number of points for training physical equations will improve the reconstruction accuracy. The proposed approach can correctly reconstruct the real tornado vortex with a relative error of less than 10%.

AgRIS: wind-adaptive wideband reconfigurable intelligent surfaces for resilient wireless agricultural networks at millimeter-wave spectrum

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due to the relatively small wavelength at this frequency band. To improve coverage in the mmWave spectrum in agricultural settings, reconfigurable intelligent surfaces (RISs) are a promising solution with low energy consumption and high cost efficiency when compared to half-duplex active relays with multiple antennas. To ensure link resiliency under dynamic channel behavior, an adaptive RIS for broadband wireless agricultural networks (AgRIS) at mmWave band is designed in this work. AgRIS relies on output from a time-series model that forecasts the short-term wind speed based on measured wind data, which is readily available in most farms. The temporal correlation between link reliability and wind speed is demonstrated through extensive field experiments. Our simulation results demonstrate that AgRIS with a small footprint of 11 × 11 elements can help mitigate the adversarial effects of wind-induced signal level drop by up to 8 dB and provides high energy efficiency of 1 Gbits/joule.

Analysis of extreme wind wave based on weibull and fisher tippett I (gumbel) distribution

Extreme waves are an important factor in coastal structures both offshore structures and onshore structure design. However, due to the difficulty of measuring ocean waves, many areas still do not have measured wave height data. Data processing is required to transform the measured wind data into wind-wave value in order to solve this issue. This study aims to analyze extreme waves based on wind data from BMKG Panjang and BMKG Radin Inten II stations. For analysis of determination of extreme waves can be done by using Fisher Tippet Type I (Gumbel) and Weibull methods. The result show that Weibull method gives the higher value than Gumbel method, with the maximum percentage of 43.04% at the 2-year return period.

An approach to assess offshore wind power potential using bathymetry and near-hub-height reanalysis data

Offshore wind power is emerging option in wind industry due to relatively consistent wind behavior over ocean. This study aims to access offshore wind resource employing a novel approach using bathymetry data, in-situ measured and 5th generation European Reanalysis (ERA-5) wind data. The measured wind data for four near-coast locations was used in Exclusive Economic Zone (EEZ) of Pakistan. The percentage bias for wind speed and wind direction of ERA-5 data compared to measured data for coastline location is −0.2% and −0.9% respectively, which shows suitability of ERA-5 data for offshore resource assessment with acceptable accuracy. Annual mean wind speed and wind power density of the EEZ is 6.1 m/s and 263 W/m2 respectively. The northeastern part of the EEZ near coastline of Sindh province is most suitable for offshore wind farms. An offshore wind farm site suitable for monopile foundation having wind power class as Fair was selected. The performance of more than 400 commercial wind turbines was evaluated and a 4.5 MW turbine having highest net capacity factor (NCF) was selected. The capacity factor of suggested wind farm having installed capacity of 540 MW is computed as 55% which can avoid 1.45 million tons in CO2eq emissions annually.

Evaluation and impact factors of Doppler wind lidar during Super Typhoon Lekima (2019)

Doppler wind lidar (DWL) has been widely employed in the wind energy industry. However, the evaluation of DWL winds under typhoon conditions is still rare. This study evaluated the accuracy of wind data measured by two types of DWLs (WindPrint S4000 and WindCube V2), and investigated the impact of three environmental factors (precipitation intensity, relative humidity and drift distance of sounding balloons) on the DWL-measured wind speed and direction. Data were collected from the joint observation at Baoshan, Zhoushan and Taizhou during the passage of Super Typhoon Lekima in 2019. The DWL observations were compared with measured data from balloon-borne radiosonde released at the same location. The results show that the optimal agreement was found with the 1-min average DWL-measured winds. The DWL data missing rate decreases with the increase of signal-to-noise ratio and height from the surface. The applicability of DWL is poor when the precipitation intensity is larger than 50 mm h−1. The DWL wind speed and wind direction biases significantly increase when the relative humidity exceeds 85% and 90%, respectively. Moreover, the measured wind data of the balloon-borne radiosonde are not reliable when the horizontal drift distance is larger than 2 km.

Monitoring of Wind Effects on a Super-Tall Building under a Typhoon

Field measurements are critical to further understand the structural behavior of super-tall buildings under strong wind actions. This paper presents field measurements that reflect the wind characteristics and wind effects on Leatop Plaza under Typhoon Vicente. Wind field characteristics, including the turbulence intensity, gust factor, and power spectral density of wind speed in an urban area, were obtained on the basis of a statistical analysis of measured wind data. Subsequently, measured wind-induced accelerations were used to evaluate the dynamic characteristics of the building and the effects of wind on it. On the basis of the first several modes, the modal properties, i.e., the natural frequency and damping ratio, were identified via the fast Bayesian fast Fourier transform method and compared with those identified using the stochastic subspace method. The discrepancy between the identified results and finite element model predictions is presented and discussed. Finally, the variation in the modal parameters with respect to time and the vibration amplitude was analyzed while considering the associated posterior uncertainty.

Research on neural network wind speed prediction model based on improved sparrow algorithm optimization

The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.