Constant Wind Speed Research Articles

Measurement of sand particle motion speed in multiphase wind-sand flow based on piezoelectric ceramic sensors

Abstract Utilizing Pb(ZrTi)O3-5A piezoelectric ceramic (hereafter referred to as PZT-5A) as sensors, we studied the movement speed of sand particles in multiphase wind-sand flows. We developed a mathematical model that effectively links the impact force of sand grains with the output voltage of PZT-5A, incorporating factors such as the piezoelectric coefficient and sand particle characteristics. Additionally, we proposed a new method to accurately determine the elastic recovery coefficient of sand particles using PZT-5A sensor measurements and experimental setups, which is significant for the field of material science. Wind tunnel experiments revealed that at heights ranging from 0.1 to 1.0m, sand particle speeds range from 52.8% to 91.4% of wind speeds. As wind speed increases to 15m/s, sand particle speed nears 91.4% of wind speed. Yet, at a constant wind speed, sand speed drops as sediment discharge rises. This research offers fresh insights into sand particle dynamics in wind-sand contexts.

Thermal effect of VIPV modules in refrigerated trucks

The integration of Photovoltaic (PV) modules within the outer structure of commercial transporters offers a great potential as an additional source of energy that can be utilized, for example, directly by the drive train of the vehicle, or by accessory equipment. However, to understand the benefits of such a system, the effect and relevance of some thermodynamic variables introduced by the Vehicle Integrated Photovoltaic (VIPV) system must be assessed. Particularly, the share of the solar irradiance that cannot be converted into electricity by the solar cell and is instead transformed into heat must be considered for certain applications such as the transportation of goods that require a controlled temperature. A one-dimensional thermal simulation model based on a Resistance-Capacitance methodology was created and validated experimentally. The model was used to predict the thermal behavior of the box-body of a truck for a representative year in three cities in Europe (Stockholm, Freiburg, and Seville), with a known Bill of Materials (BOM), and a set of given assumptions and constraints. It was found that, under the simulated conditions laid in this study, the additional heat generated by the PV modules that manages to go through the insulation material and reach the refrigerated cargo area, may raise the temperature of the contained air by an average of 0.36 °C, 0.5 °C, and 0.67 °C, respectively for the observed cities, and as much as 3.12 °C, 2.98 °C and 2.61 °C. When forced convection caused by the movement of the truck was applied into the model (with a constant wind speed of 50 km/h), the temperature of the solar cell dropped significantly, which, for the case of Freiburg, meant an increase in the contained air temperature of a maximum of 0.6 °C. A refrigeration system was subsequently considered for a target air temperature of 2 °C and −18 °C, and it was found that for the simulated scenarios, the harvested solar energy could easily offset the additional energetic demand caused by the PV system, and, in most cases, completely balance out the total annual demand of the chiller.

Exploring the impact of different inflow conditions on wind turbine wakes using Large-Eddy Simulations

The ever-growing demand for renewable energy, driven by cost-effectiveness and minimal ecological impacts, has resulted in the deployment of larger wind turbines with rotor diameters surpassing 200 m. This underscores the importance of a thorough understanding of flow dynamics to optimize operational efficiency in diverse atmospheric inflow scenarios. Understanding the intricate impact of atmospheric conditions, including wind shear and turbulence, on wind turbine wakes is crucial for optimizing wind farm layouts and performance, influencing wake evolution, turbine loads, and power output. This research focuses on bridging the gap between idealized inflow scenarios and real-world atmospheric inflow conditions by systematically integrating linear shear, turbulence and the logarithmic wind shear profile into the uniform inflow conditions and analyzing the wake behind the IEA-15 MW wind turbine. To specifically examine inflow effects, a constant hub height wind speed was maintained through a velocity controller. The study focuses on analyzing the wake’s flow field and providing insights into its recovery process. It was found that turbulence plays a critical role in a faster wake recovery as well as increasing the power production of the turbine for sheared inflows and the wind speed selected.

FRT capability improvement of a wind power system using support vector regression and deep neural network models

ABSTRACT The increasing energy demand and environmental conditions have led many countries to favor renewable energy,such as wind and solar,over conventional power plants. As per the Central Electricity Authority (CEA), India’s wind power capacity has grown substantially, reaching 45,154 MW in 2023, from 22,465 MW in 2013. However, integrating wind generators to an existing power system network can reduce transient stability during faults. To prevent wind turbine disconnection during faults, Indian grid code authorities mandate Fault Ride Through (FRT) or Low Voltage Ride Through (LVRT) capability for wind turbines. This paper proposes Dynamic Voltage Restorer (DVR) for enhancing the FRT capability of a constant speed wind turbine. Analysis and simulation are done on a fixed speed wind turbine employing Squirrel Cage Induction Generator (SCIG) under balanced and unbalanced voltage sags. Performance of DVR with two Artificial Intelligence-based controllers, Support Vector Machines(SVM) Regression Model Based Supervised Machine Learning algorithm, and Deep Neural Network (DNN) controller, is analysed and compared. The SVM Regression algorithm, achieves 100% voltage sag compensation under unbalanced voltage sags, reduces torque pulsations to 25% and maintains rotor speed at rated values, enabling the turbine to remain connected to the grid. Additionally it reduces Total Harmonic Distortion to 2%.

Study on the Improvement of Droplet Penetration Effect by Nozzle Tilt Angle under the Influence of Orthogonal Side Wind.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

Development and verification of a method to simulate non-stationary vehicle interior wind noise

As speeds and directions of the vehicle and wind change, the time-varying flow creates variations in wind noise, which can be referred to as non-stationary wind noise. To investigate people's perceptions of non-stationary wind noise inside the vehicle, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary wind noise recordings taken at several constant wind speeds and directions to form functions that relate the 1/3 octave sound pressure level with wind speed and direction. These functions are used to create time-varying filters based on provided time histories of wind speed and direction. To reduce the cost of taking many stationary measurements, an improved method was investigated. At each yaw angle, one speed sweep wind tunnel measurement was used to estimate the relationship between sound pressure level and wind speed. Two partially correlated white noise signals were then filtered to simulate binaural sounds that had a similar coherence structure between the left and right ear sounds to that observed in binaural measurements in the vehicle. The accuracy of the simulations was validated by comparing wind noise simulations to vehicle interior noise measured in the wind tunnel and on the road.

Modelling and Simulation of Grid Connected Wind Turbine Induction Generator for Windfarm

As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.

3D pressure-corrected ballistic extrapolation of solar wind speed in the inner heliosphere

Solar wind parameters at different locations in the inner heliosphere can be estimated using various solar wind extrapolation methods. The simple ballistic method extrapolates solar wind parameters from the point of measurement to a chosen heliospheric position by assuming that major solar wind structures are persistent and arrive relatively unaltered to the target position. The method considers the rotation period of the Sun while assuming a constant solar wind speed during radial propagation. We improve the simple ballistic model by considering the interaction between the slow and the fast solar wind with a pressure correction during the propagation. Instead of extrapolating from the position of a single spacecraft, we apply this pressure-corrected ballistic method to 2D speed maps of the solar source surface available from solar coronal models to determine the solar wind speed in the inner heliosphere in 3D, between latitudes of ±50°. We also take into account the effects of the solar differential rotation in our model. Our method is simple and fast, and it can be applied to different source surface datasets. The results of our model are validated with in situ data from the ACE spacecraft. We find that the pressure-corrected ballistic method can give accurate predictions of the solar wind in 3D.

A catalogue of observed geo-effective CME/ICME characteristics

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), to improve our ability to forecast their propagation characteristics and mitigate the risks to our technologically driven society. The essential part of achieving this goal is to assess the performance of forecasting models. To this end, the quality and availability of suitable data are of paramount importance. In this work, we merged publicly available data of CMEs from both in-situ and remote observations in order to build a dataset of CME properties. To evaluate the accuracy of the dataset and confirm the relationship between in-situ and remote observations, we have employed the Drag-Based Model (DBM) due to its simplicity and modest consumption of computational resources. In this study, we have also explored the parameter space for the drag parameter and solar wind speed using a Monte Carlo approach to evaluate how efficiently the DBM determines the propagation of CMEs for the events in the dataset. The geoeffective CMEs selected as a result of this work are compliant with the hypothesis of DBM (isolated CME, constant solar wind speed beyond 20 R⊙) and also yield further insight into CME features such as arrival time and arrival speed at L1 point, lift-off time, speed at 20 R⊙ and other similar quantities. Our analysis based on the acceptance rate in the DBM inversion procedure shows that almost 50% of the CME events in the dataset are well described by DBM as they propagate in the heliosphere. The dataset includes statistical metrics for the DBM model parameters. The probability distribution functions (PDFs) for the free parameters of DBM have been derived through a Monte Carlo-like inversion procedure. Probability distribution functions obtained from this work are comparable to PDFs employed in previous works. The analysis showed that there exist two different most probable values (median values) of solar wind speed for DBM input based on slow (wslow ≈ 386 km/s) and fast (wfast ≈ 547 km/s) solar wind type. The most probable value for the drag parameter (γ ≈ 0.687 × 10−7 km−1) in our study is somewhat higher than the values reported in previous studies. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. Additionally, possible CME events are identified where the DBM prediction is not valid due to model limitations and higher uncertainties in the input parameters. These events require further thorough investigation in the future.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics

AbstractWith the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.

Wandering albatrosses exert high take-off effort only when both wind and waves are gentle

The relationship between the environment and marine animal small-scale behavior is not fully understood. This is largely due to the difficulty in obtaining environmental datasets with a high spatiotemporal precision. The problem is particularly pertinent in assessing the influence of environmental factors in rapid, high energy-consuming behavior such as seabird take-off. To fill the gaps in the existing environmental datasets, we employed novel techniques using animal-borne sensors with motion records to estimate wind and ocean wave parameters and evaluated their influence on wandering albatross take-off patterns. Measurements revealed that wind speed and wave heights experienced by wandering albatrosses during take-off ranged from 0.7 to 15.4 m/s and 1.6 to 6.4 m, respectively. The four indices measured (flapping number, frequency, sea surface running speed, and duration) also varied with the environmental conditions (e.g., flapping number varied from 0 to over 20). Importantly, take-off was easier under higher wave conditions than under lower wave conditions at a constant wind speed, and take-off effort increased only when both wind and waves were gentle. Our data suggest that both ocean waves and winds play important roles for albatross take-off and advances our current understanding of albatross flight mechanisms.

Wandering albatrosses exert high take-off effort only when both wind and waves are gentle.

The relationship between the environment and marine animal small-scale behavior is not fully understood. This is largely due to the difficulty in obtaining environmental datasets with a high spatiotemporal precision. The problem is particularly pertinent in assessing the influence of environmental factors in rapid, high energy-consuming behavior such as seabird take-off. To fill the gaps in the existing environmental datasets, we employed novel techniques using animal-borne sensors with motion records to estimate wind and ocean wave parameters and evaluated their influence on wandering albatross take-off patterns. Measurements revealed that wind speed and wave heights experienced by wandering albatrosses during take-off ranged from 0.7 to 15.4 m/s and 1.6 to 6.4 m, respectively. The four indices measured (flapping number, frequency, sea surface running speed, and duration) also varied with the environmental conditions (e.g., flapping number varied from 0 to over 20). Importantly, take-off was easier under higher wave conditions than under lower wave conditions at a constant wind speed, and take-off effort increased only when both wind and waves were gentle. Our data suggest that both ocean waves and winds play important roles for albatross take-off and advances our current understanding of albatross flight mechanisms.

Evaluation of magnetic gear parameters for autonomous wind installation with changing wind speed

Purpose. To evaluate the parameters and characteristics of the magnetic gearbox at constant and variable wind speed as part of an autonomous wind power plant. Methodology. Analysis of existing methods of mathematical modeling of magnetic gearboxes. Numerical methods of mathematical modeling based on finite elements. Numerical methods of processing the obtained results. Findings. A two-dimensional field mathematical model has been developed to estimate the parameters and characteristics of the magnetic gearbox at variable and constant wind speed. With the help of the developed model, the electromagnetic field, parameters and characteristics of the magnetic gearbox were analyzed both at a constant and at a variable speed of rotation. In particular, it was found that the magnetic system of the reducer does not reach saturation, while the average values of magnetic induction in the structural elements of the reducer remain within the permissible values for the used materials and grades of steel. The obtained data on the average values of the electromagnetic moments of the outer (high-speed) rotor and the inner rotor make it possible to understand the relationship between them and their influence on the operation of the system. A conclusion was made about the uneven distribution of induction along the middle line in the air gap, which is caused by the presence of a modulator in the design of the reducer. In addition, according to the obtained results, the change in wind speed affects the speed of rotation of the magnetic gearbox and the magnitude of the electromagnetic moments of the high-speed and low-speed rotors. Such information helps to understand the dynamics of system operation under different environmental conditions. The results obtained in the study are important for further improvement of the magnetic reducer design and ensuring more efficient operation of the low-power wind turbine. They can be used to optimize the structure and increase its productivity, which will contribute to the development of wind energy and the green energy industry in general. The average value of the electromagnetic moment of the outer (high-speed) rotor is about 7.4 N∙m, while for the inner rotor the value of the electromagnetic moment is about 66 N∙m. The ratio of the moments of the outer and inner rotors is 8.92, which correlates with the reduction factor of the gear under study. Originality. A two-dimensional field mathematical model has been developed, which takes into account the change in the parameters and characteristics of the magnetic gearbox when the wind speed changes and the random rotation speed of the wind generator rotor. Practical value. The results of the research of the magnetic reducer for a low-power wind turbine have an important contribution to the development of wind energy, contribute to increasing the efficiency and reliability of energy production, and contribute to the creation of a more sustainable and environmentally friendly energy complex.

Leading Edge Radius Effects on VAWT Performance

Numerous studies have been conducted to investigate effect of blade geometry of vertical axis wind turbine performance. Most of the evaluations have focused on the airfoil series and airfoil geometry parameters such as thickness and camber of the airfoil. Few studies have examined the effect of other blade geometry parameters on the vertical axis wind turbine performance. In the present study, the effect of geometric change in leading-edge radius (LER) of a vertical axis wind turbine performance has been numerically studied. Hence, modified NACA 0021 airfoil profiles were created using the geometric method (CST). Then, the flow behavior around a Darrieus vertical axis wind turbine was simulated under the influence of the reduction and set-up coefficients of the leading-edge radius at a constant wind speed of 9 m/s and a tip speed ratio of 1.5 to 3.5 using the computational fluid dynamics. Additionally, the effects of the examined parameter (leading-edge radius) on fluid flow and aerodynamic performance coefficients, including the coefficients of power and torque, were investigated. The results indicated that the leading-edge radius affected the near wake flow of the turbine, and the optimization of leading-edge radius parameter controls the dynamic stall and reduces the formation of a vortex. Finally, the optimization of LER revealed that at 20% reduction in the LER the performance of the turbine at tip speed ratio of 1.5 was increased by more than 50%. This reinforces the self-starting capability of a Darrieus wind turbine.

Stochastic analysis of galloping piezoelectric energy harvesters under turbulent flow conditions based on the probability density evolution method

Harvesting wind energy to power wireless sensors is a very promising technology. Galloping piezoelectric energy harvesters (GPEHs) are considered to be an excellent solution that can be miniaturized but high efficiency. In order to better understand the working mechanism and determine the design method, it is very necessary to develop accurate theoretical model of GPEHs. In conventional method, the wind was usually simplified as uniform flow with constant wind speed. In practice, however, the wind is turbulent flow that consists of uniform and fluctuating components. The influence of fluctuating component on GPEHs is usually neglected in existing literatures, which will lead to significant errors. To address this issue, this work proposes a stochastic method for GPEHs based on the probability density evolution method (PDEM). Aerodynamic coefficients are regarded as random variables to describe the random excitation induced by turbulent flow. The confidence bands of the output power and the cut-in wind speed of GPEHs under turbulent flow condition are predicted, which satisfy specific probability density distribution. The probabilistic concentrated phenomenon of the output power at low wind speed and far from the optimal resistance is observed. Its mechanism is explored in detail. In addition, a parametric study is conducted to highlight the influence of the variability of the aerodynamic coefficients and the probability distributions of the aerodynamic coefficients on the output power. The proposed method is verified by Monte Carlo Simulation (MCS) and experiment.

Soil erosion and nutrient loss due to changes in rainfall intensity under different wind directions

AbstractIn nature, wind often occurs simultaneously with rainfall, causing nutrient loss in soil and water on slopes. However, the influence of different rainfall intensities combined with different wind directions on soil erosion and nutrient loss remains unclear. This study utilized wind‐driven rainfall laboratory flume experiments to analyse the influence of different wind directions (windward, leeward and crosswind) and rainfall intensities (30, 60 and 90 mm h−1) with constant wind speed (5 m s−1) on slope runoff, sediment yield and the associated losses of different forms of N ( , ) and P ( ‐P). The response of soil erosion, N and P loss to rainfall changes with different wind directions. When the rainfall intensity was constant, runoff, sediment yield and sediment‐associated total nitrogen (TN) and total phosphorus (TP) losses on slopes showed a trend of leeward > crosswind > windless > windward, whereas runoff‐associated TN and TP losses showed a trend of windward > crosswind > windless > leeward. Runoff, sediment yield and sediment‐associated TN and TP losses on leeward slopes were 1.09–1.17, 1.25–1.44, 1.33–1.62, and 1.40–1.66 times than windless slopes. Moreover, with constant wind direction, the runoff, sediment yield and associated N and P losses increased significantly with increasing rainfall intensity. When the rainfall intensity increased from 30 to 60 and 90 mm h−1, the runoff, sediment yield and associated TN and TP losses increased 2.02–2.28, 3.65–5.35, 3.96–34.39 and 4.28–8.45 times, respectively. The effects of rainfall on soil erosion and nutrient loss were significantly greater than those of wind. The loss of different forms of N and P associated with runoff and sediment was consistent with that of TN and TP losses. However, with an increase in rainfall intensity, the growth rates of runoff, sediment yield and associated N and P losses exhibited a gradually weakening trend for all wind directions. An increase in rainfall intensity enhanced the vertical vector force of wind‐driven rainfall, which increasingly decreased the angle between the rain incidence and the normal line of the horizon; therefore, the direction of the rain erosion force gradually approached that of windless conditions.

On a Problem of Marine Current Velocity Estimation from Microwave Radar Data

The paper is devoted to the problem of estimating marine current velocity from microwave radar data, one of the important tasks of sea remote sensing. We present some results of simultaneous measurements of radar scatterers velocities and sea current and wind velocities. Radar scatterers velocities were measured using a dual-polarized (VV/HH) Doppler radar operating in S/C/X bands. The experiments were carried out in the coastal zone of the Black Sea at moderate incidence angles (30–70 degrees). It was obtained that the subsurface current velocity (current in the upper layer of ten centimeters) retrieved from the Bragg component of the radar return can be used to estimate changes in marine current (a part of the sea current that is not related to the wind) at constant wind speed. The subsurface current velocity is found as a vector sum of the current velocity measured at a depth of 1 m and the wind component equal to 1–3% of the wind speed. Possibilities of estimating the current velocity from VV/HH/PD data are analyzed.

Speed control of a DFIG-based wind turbine using a new generation of ADRC

ABSTRACT This paper represents a new generation of adaptive disturbance rejection control (ADRC) which is more robust against disturbances than conventional ADRC. The non-derivability of the fal function employed in the traditional ADRC has negative impacts on its operation, so alternate functions will be used which are derivable at all segments. In this regard, odd hyperbolic and trigonometric functions were employed. The performance of the proposed structure was investigated to control the doubly fed induction generator (DFIG) speed. To enhance the efficiency of the suggested ADRC, fractional-order calculations and fuzzy logic were utilized simultaneously. The results in MATLAB platform indicate that: 1) With constant wind speed and changing reference speed, using new fal functions in fuzzy fractional-order ADRC (FFOADRC) has improved the performance compared to the PI regulator and FFOADRC with default fal function. 2) During wind speed variations and using new fal functions, the DFIG speed reaches the final steady amount as over damping, while this condition is critical damping when using FFOADRC with default fal function. 3) During network voltage reduction, using new FFOADRCs leads to fewer oscillations in the stator flux, DC-bus voltage, and DFIG speed, which shows their better performance than PI regulator and traditional ADRC.

Comparison of varied complexity parameterizations in estimating blowing snow occurrences

Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.