Inflow Wind Speed Research Articles

Study on power characteristics of wind turbine on the Loess Plateau terrain based on wind tunnel experiment

In order to investigate the impact of the flow field on wind turbine power in northwest China’s Loess Plateau, a wind tunnel experiment was conducted using a self-designed model that simulated the actual terrain. By comparing experiments utilizing an ideal mountain model under varying incoming wind speeds, it was revealed that the Loess Plateau terrain resulted in an increased inflow wind speed for the wind turbine. This higher inflow wind speed led to an approximate 5% increase in output power but also caused about a 6.1% rise in power fluctuations. Through analysis of the power spectrum of the output, it was discovered that turbulence below the decoupling frequency strongly modulates the output power of the wind turbine. This study also provided insights into spatial scale ranges where incoming flow turbulence structures can modulate output power, ranging from approximately 0.3 times to 5 times the diameter of rotor blades.

Loads and fatigue characteristics assessment of wind farm based on dynamic wake meandering model

As the scale of wind farms continues to expand, the impact of the wake on the power generation and load of wind turbines is becoming increasingly prominent. This study delves into the wake characteristics, power generation, load, and fatigue behavior of an ideal layout wind farm using a dynamic wake meandering model, accounting for diverse spacing configurations, inflow conditions, and yaw angles. The findings reveal that increasing the axial spacing facilitates superior wake recovery, promoting a more evenly distributed load and reducing fatigue damage throughout the wind farm. Moreover, higher inflow wind speed or turbulence intensity exacerbates fatigue damage, especially at elevated wind, where the damage accumulates exponentially. Under yaw conditions, the wake deviates, reducing its adverse impact on downstream turbines and subsequently boosting the overall power generation of the wind farm. However, this deflection simultaneously introduces intricate and detrimental effects on the fatigue load. Notably, optimal yaw achieves a 7.9 % improvement in power generation, also resulting in the most significant fatigue damage at the blade root and tower base. This study provides theoretical and technical underpinnings for the layout optimization and control strategies of wind farms or wind farm clusters.

Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model

Wake effect is a significant factor contributing to power loss in wind farms. Studies have shown that wake steering control can mitigate this power loss. Currently, wind farm wake control strategies primarily utilize fixed yaw control due to limitations in the accuracy and efficiency of dynamic wake models. However, fixed yaw control fails to fully exploit the power improvement potential of wake steering control. Therefore, in this study, we first propose a dynamic wake model for wind farms based on the physics-guided neural network (PGNN) approach. This model can predict the dynamic wake flow field within wind farms in real time using instantaneous inflow wind speed and turbine operational states. Then, by employing the PGNN dynamic wake model as the predictive model, a wind farm dynamic wake control strategy based on the model predictive control method is proposed. To quantify the advantages of the proposed control strategy, both fixed yaw control and dynamic yaw control are tested on a wind farm with a 3 × 2 layout. Results from large eddy simulations demonstrate that the proposed dynamic wake control strategy increases the power output of the wind farm by 11.51% compared to a 6.56% increase achieved with fixed yaw control.

Machine learning to rapidly predict turbine yaw angles for wake steering

Wake steering is an important control strategy to boost power production of a wind farm. Because of computational expense and problem complexity, wind farm layouts are typically optimized assuming they will operate without wake steering. However, performance gains are possible by simultaneously optimizing wind farm layout and the yaw angles for wake steering. In this paper, we present a method to train a machine learning model to predict turbine yaw angles as a function of their position relative to other turbines in the wind farm and the inflow wind speed. This model is able to predict turbine yaw angles with an R2 value of 0.98. The model also produces turbine yaw angles with wind farm power production that is similar to yaw angles that have been directly optimized. This method to rapidly compute optimal turbine yaw angles for wake steering enables control co-design of wind farms and the associated performance increase.

Impact of motions on floating wind turbine power production

Floating offshore wind opens new possibilities for harnessing wind energy in deeper waters where it is not feasible to install traditional fixed-bottom turbines. Accessing deeper waters enables the utilization of stronger and more consistent wind resources, potentially leading to higher energy production. However, one of the challenges of floating offshore wind is the impact of increased motions on floating turbine’s power. This paper addresses this challenge by investigating wind field reconstruction and motion compensation algorithms when using a nacelle lidar to characterize floating turbine inflow wind speed. The fully instrumented TetraSpar floating demonstrator with a 3.6 MW wind turbine and a nacelle lidar, offers a unique opportunity to investigate the effect of motions on power production. Observations from real measurements are complemented with numerical simulations, highlighting that motion-compensating for mean tilt angle is an effective correction for 10 min average power performance measurements. Results showed that mean tilt angles causing the lidar beams to shift upwards, result in overestimation of the estimated hub height wind speed if no motion compensation is applied. The paper also assesses the impact of motions induced by different sea states on power production.

Turbulence coherence in wind farms: The role of turbines

Models for wind farm power fluctuations primarily focus on the impact of atmospheric turbulence. We employ large eddy simulations (LES) to demonstrate that dynamic changes in thrust (CT ) and power (CP ) coefficient affect the coherence of power fluctuations in turbine pairs. We consider various inflow wind speeds to examine the velocity and power coherence between consecutive turbine rows under three scenarios: (I) a fully developed region where all turbines operate below rated power, (II) the front row operates above rated power while the downstream row operates below rated power, and (III) both rows operate above rated power. In scenario I, the random sweeping hypothesis turbulence model by Tobin and Chamorro, JFM 855, 1116-1129 (2018) can effectively predict the coherence between the turbines. However, in scenarios II and III, the model fails to capture the simulation results. This discrepancy arises due to the operation of above-rated turbines, with dynamically varying CT and CP , which have a distinctly different effect on the flow than turbines operating with fixed CT and CP .

Study on the Evolution Pattern of Complex Topographic Flow Field in Typical Tableland and Hogback Ridge

The flow field in complex mountainous wind farms is highly intricate, affecting the accuracy of wind resource assessments and the reliability of wind turbine operation. In order to study the evolving characteristics of the flow field in typical complex terrains under different inflow wind directions and wind speeds, this paper, based on the open-source software OpenFOAM, adopts a neutral atmospheric boundary layer model combined with Reynolds-averaged turbulence model to construct a high-precision flow field model for complex terrains. The research reveals that this model requires fewer computational resources and can effectively simulate the flow characteristics around complex terrains. Wind resource distribution in tableland terrains and hogback ridge terrains is more influenced by the height of the mountains, and the recovery speed of the wake behind the mountains decreases with a reduction in slope. In hogback ridge terrains, under a wind direction of 0 °, the wind acceleration effect at the top of the hogback ridge is stronger than under a wind direction of 270°, as it experiences more intense compression from the mountains. This paper can provide reference for micro-siting in complex terrain.

Predictive capability of an improved AD/RANS method for multiple wind turbines and wind farm wakes

The actuator disc (AD) model combined with the Reynolds-averaged Navier-Stokes (RANS) model has been demonstrated as the most effective approach for simulating wind farm wakes due to its computational efficiency and acceptable accuracy. However, challenges remain in defining the reference inflow wind speed for the AD model and identifying a robust turbulence model for the RANS method. To solve these issues, two strategies for establishing the AD model (AD-up1D model and AD-local model), along with four turbulence models (standard k-ε, SST k-ω, linear pressure-strain RSM and modified RSM), are comprehensively validated here to predict multiple wakes within two representative wind farms. The temporal variability of multiple turbines’ power output and the significant impact of wind direction on the power is particularly emphasized. Overall, this study indicates that the proposed AD-local/Mod RSM method facilitates a realistic wake recovery in the cumulated wake flow and agrees well with the field measurements. It consistently performs well, never ranking last in any test cases, with percentage deviations ranging from 1.8% to 9.0%. Most importantly, this study evaluates the strengths and weaknesses of each AD/RANS method and provides recommendations for wind farm design purposes.

Enhanced recovery caused by nonlinear dynamics in the wake of a floating offshore wind turbine

An experimental study in a wind tunnel is presented to explore the wake of a floating wind turbine subjected to harmonic side-to-side and fore–aft motions under laminar inflow conditions. The wake recovery is analysed as a function of the frequency of motion $f_p$ , expressed by the rotor-based Strouhal number, $St = f_p D / U_{\infty }$ ( $D$ is the rotor diameter, $U_{\infty }$ the inflow wind speed). Our findings indicate that both directions of motion accelerate the transition to the far-wake compared with the fixed turbine. The experimental outcomes confirm the computational fluid dynamics results of Li et al. (J. Fluid Mech., vol. 934, 2022, p. A29) showing that sideways motions lead to faster wake recovery, especially for $St \in [0.2, 0.6]$ . Additionally, we find that fore–aft motions also lead to better recovery for $St \in [0.3, 0.9]$ . The recovery is closely linked to nonlinear spatiotemporal dynamics found in the shear layer region of the wake. For both directions of motion and $St \in [0.2, 0.55]$ , the noisy wake dynamics lock in to the frequency of the motion. In this synchronised-like state, sideways motions result in large coherent structures of meandering, and fore–aft movements induce coherent pulsing of the wake. For fore–aft motion and $St \in [0.55, 0.9]$ , the wake shows a more complex quasiperiodic dynamic, namely, a self-generated meandering mode emerges, which interacts nonlinearly with the excitation frequency $St$ , as evidenced by the occurrence of mixing components. The coherent structures grow nonlinearly, enhance wake mixing and accelerate the transition to the far-wake, which, once reached, exhibits universal behaviour.

A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines

Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake control and load assessment in wind farms. This paper proposes a real-time dynamic wake prediction model for wind turbines based on a physics-guided neural network. The model can predict the instantaneous dynamic wake field under various operating conditions using only the inflow wind speed as input. The model utilizes Taylor's frozen-flow hypothesis and a steady-state wake model to convert instantaneous inflow wind speed and turbine parameters into neural network input features. A deep convolutional neural network then maps these features to desired wake field snapshots, enabling dynamic wake predictions for wind turbines. To train the model, we generated approximately 255 000 instantaneous flow field snapshots of single-turbine wakes using the large eddy simulation, covering different thrust coefficients and yaw angles. The model was trained using the supervised learning method and verified on the test set. The results indicate that the model can effectively predict the dynamic wake characteristics, including the dynamic wake meandering and the wake deflection of the yawed turbines. The model can also assess both the instantaneous wake velocity and the instantaneous wake center of a wind turbine. At a thrust coefficient of 0.75, the root mean square error for the predicted instantaneous wake velocity is around 6.53%, while the Pearson correlation coefficient for the predicted instantaneous wake center can reach 0.624. Furthermore, once the model is trained, its prediction accuracy does not decrease with the increase in the time span.

Active control of cavity buffeting noise using a dielectric barrier discharge plasma actuator

This work examines the active control of vehicle buffeting noise using a dielectric barrier discharge plasma actuator (DBD-PA). First, the buffeting noise characteristics of a cavity under different inflow wind speeds are established through a wind tunnel test, and the effectiveness of the buffeting noise measurement results is verified. Various types of actuator layouts are designed based on specific combinations of parameters, and the interval optimization algorithm is used to identify the layout type of actuators that have the best equalization control effect under different inflow wind speeds. The experimental results regarding buffeting noise characteristics show that the buffeting frequency and the peak value of the sound pressure level increase with the increasing of wind speed. The control results show that the overall control effect of the reverse layout of the span-wise DBD-PA (SDBD-PA) is better than that of the positive layout. Consequently, the control effects of the SDBD-PA layout decrease in the front, rear, and oblique directions. A DBD vortex generator (DBD-VG) has no control effect when it is arranged in the front but has a certain control effect when it is arranged in the rear. The control effect is better with longer DBD-VGs. The results of running the interval optimization algorithm at different incoming wind speeds are as follows: reverse-front-SDBD-PA and long-rear-DBD-VG can achieve the best equalization control effect of cavity buffeting noise. The average noise reduction values are 5.26 dB and 4.42 dB, respectively, and the maximum noise reduction values are 7.48 dB and 5.79 dB, respectively.

Design approach of thrust-matched rotor for basin model tests of floating straight-bladed vertical axis wind turbines

Rotor redesign approaches have been widely proposed to solve the thrust mismatch issue caused by scaling effects for basin model tests of horizontal axis floating wind turbines (FWTs). However, limited basin model tests utilized the thrust-matched rotor (TMR) to accurately evaluate the aerodynamic loads applying to the vertical axis FWTs. This paper described the detailed design approach of the TMR of floating straight-bladed vertical axis wind turbines (VAWTs) with a rated power of 5.3 MW. First, the AG455 airfoil was selected to replace the NACA0018 airfoil. AG455 airfoil can show a larger lift coefficient and a smaller drag coefficient at low Reynolds number. On this basis, the load distribution match algorithm was used to assign the blade pitch angle and chord length at each section of the blade. This method takes the spanwise load and load change rate of model-scaled blade and full-scaled blade as the constraint conditions. By adopting this method, the rotor thrust can be tailored to match the prototype values across a wide range of tip speed ratios. This design approach proves advantageous in assessing the aerodynamic performance of VAWTs under varying inflow wind speeds and unsteady wind conditions. The redesigned TMR model under low Reynolds number can meet Froude similarity criterion, which is helpful to improve the accuracy of vertical axis FWT model tests in the wave basin.

Wake interaction between two spar-type floating offshore wind turbines under different layouts

Limited by layout space and manufacturing costs, wake interaction in offshore wind farms is inevitable and can have adverse effects on the performance of downstream wind turbines. To gain a better understanding of the wake interaction between Floating Offshore Wind Turbines (FOWTs), this paper conducts coupled aero-hydrodynamic simulations for two spar-type FOWTs under different layouts. The Unsteady Actuator Line Model (UALM) is used to analyze the unsteady aerodynamic loads of the wind turbine, while the hydrodynamic responses of the floating support platform are obtained using the Computational Fluid Dynamics (CFD) method. To predict the aero-hydrodynamic performance of the FOWT under combined wind and waves, an in-house CFD code developed at Shanghai Jiao Tong University (SJTU), called FOWT-UALM-SJTU solver, is utilized. First, grid convergence test and time step sensitivity study are performed to determine appropriate simulation parameters. Subsequently, numerical simulations of two FOWTs under tandem and offset layouts are conducted to investigate the influence of wake interaction on the performance of downstream FOWT. The dynamic responses of the FWOT, including aerodynamic loads, platform motions, and wake characteristics, are analyzed in detail. From the simulation results and discussions, several conclusions are drawn. Both platform motions and wake interaction contribute to an increased variation range of inflow wind speed experienced by the downstream FOWT, thereby exacerbating the instability of its aerodynamic loads. Under the tandem layout, the platform motions of the upstream FOWT and the downstream FOWT exhibit opposite effects on the aerodynamic loads of the downstream FOWT. Moreover, platform motions increase turbulence intensity in the wake region, accelerating wake velocity recovery and widening the wake width.

A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling

Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning

This study describes a deep reinforcement learning (DRL) based decentralized yaw optimization method to maximize the power production of wind farms. Specifically, we apply the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to design agents separately for each turbine in the wind farm to make yaw decisions independently. This design allows setting different yaw rewards for the agents to promote faster and better convergence of MADDPG. To test the control effect of the proposed method, a novel analytical dynamic wake model is derived first, which can dynamically reflect the wake propagation of the wind turbine after the inflow wind speed, axial induction factor, and yaw angle change. Then, the static and dynamic characteristics of the proposed wake model are verified. The wind farm simulation is realized through the dynamic wake model, which provides an interactive environment for the simulation experiments to test the effect of the proposed DRL-based decentralized yaw optimization method. The results show that the proposed method can significantly increase the power generation of the wind farm, and the set yaw reward helps to guide MADDPG to converge to the optimal strategy.

Multi‐hazard fragility assessment of monopile offshore wind turbines under earthquake, wind and wave loads

AbstractThis study establishes a multi‐hazard probabilistic assessment framework for assessing the integrity of monopile offshore wind turbines (OWT) under the stochastic coupled effect of wind, wave and earthquake loading. The procedure deals with the entire operational range of inflow wind speed (i.e., 3–25 m/s), for which the probability of failure under multi‐hazard excitations is found to be non‐negligible. Numerical analysis is performed by implementing nonlinear finite‐element models of the OWT developed in OpenSees. The dynamic response of the OWT system under wind‐ and wave‐load combinations is individually validated against those obtained from the aero‐hydro‐servo‐elastic simulator OpenFAST. Following the Latin‐hypercube approach, a cloud‐based assessment procedure is then performed with an ensemble of 300 earthquake ground motions, from which the multi‐hazard performance of the OWT regarding the serviceability limit state (SLS) and the ultimate limit state (ULS) can be evaluated. The epistemic uncertainty associated with various loads, structural properties, and soil conditions is also accounted for. Based on this probabilistic assessment framework, the sensitivity of the resulting OWT fragility surfaces to different statistical regression methods and wind—ground motion intensity measure pairs (IM‐pairs) is further scrutinised. Regression methods are comparatively evaluated. The efficiency, practicality, proficiency and sufficiency of various IM‐pairs are examined for the purpose of assessing operating OWT multi‐hazard fragility functions. The optimum IM‐pair is then employed in a trained Gaussian Process Regression (GPR) scheme for cloud data regression to assess the multi‐hazard fragility of the system. The derived multi‐hazard fragility function shows that the contribution of seismic forces in structural demand for a design‐level earthquake is comparable to those caused by operational‐level wind and wave loads.

Prediction of aerodynamic performance of NREL offshore 5‐MW baseline wind turbine considering power loss at varying wind speeds

AbstractIn this study, a computational fluid dynamics (CFD) model was developed to simulate the aerodynamic performance of the National Renewable Energy Laboratory (NREL) offshore 5‐MW baseline wind turbine with single rotor and full wind turbine. Using statistical methods, the relation between pitch angle and performance when the speed is above the rated wind speed was analyzed; furthermore, other published data were compiled to establish the functional equations of power, thrust with various inflow wind speeds, and pitch angles. In addition, according to shape optimization based on parametric modeling, the two‐dimensional cross‐section of the wind turbine blade can be defined through a metasurface approach, and the three‐dimensional surface modeling of the wind turbine blade, nacelle, and tower is completed using the nonuniform rational B‐splines (NURBS) interpolator. In terms of aerodynamic simulation, the unstructured polygon mesh was used herein to discretize the space and simulate the highly curved and twisted surfaces of the blade. In this study, the compact and accurate mesh form obtained through a grid independence test will be used to analyze the distribution of the pressure coefficient, shear stress coefficient, and limited streamline on the blade surface at various inflow wind speeds and pitch angles to understand the differences between different turbulence models and the causes of power and thrust attenuation at high inflow wind speeds. In addition, the phenomena of blade‐tip vortices, dynamic stall, blade loading, and the interaction between nacelle and tower were collectively explored.

Numerical simulation on exploring influencing factors of wind-driven natural ventilation effect in large indoor dock

The shipbuilding industry is booming and the health problems of workers caused by the harsh indoor dock environment force us to explore efficient and reasonable ventilation methods suitable for large workshops. Due to the strong specificity of large workshops, general or local ventilation methods cannot be universally applied. It has great potential and good economy to improve indoor environment by changing natural ventilation design. Computational fluid dynamics (CFD) has gradually become a powerful tool for predicting indoor and outdoor airflow organization and optimizing indoor ventilation. This paper adopts CFD to study the effect of the inflow wind speed, the position of the side wall shutters, the area ratio and form of roof ventilators on the effectiveness of natural ventilation in a large shipyard driven by wind pressure. The results show that the influence on total ventilation volume is more obvious when the intake side is shaded than the exhaust side. Different incoming wind speeds will affect the wind pressure at the ventilation position, which is the decisive external factor affecting the natural ventilation of docks. When the area ratio of roof ventilators increases to a certain extent, its continued increase has an insignificant effect on the total ventilation volume. The influence of changing the arrangement of the roof ventilator on the natural ventilation effect can be neglected when the area ratio is kept constant.

GRMWBO: design of a general-purpose repowering model for wind farms via bioinspired optimization

Wind farm optimization requires efficient design of placement models which can assist in identification of deployment locations for higher conversion efficiency. To perform this task, various methods are proposed by researchers, and most of them are applicable for new deployments. Due to limited availability of wind farm sites and cost constraints, it is impractical to replace old equipments. Thus, repowering of wind farms is necessary, and must be planned under existing deployment constraints. Existing repowering methods apply static modelling techniques, which limits their scalability and usability for real-time deployments. To overcome this limitation, a novel general-purpose repowering model via bioinspired optimization is discussed in this text. The model proposes design of a genetic algorithm that considers inflow wind speed, wind direction angle, height of hub, surface roughness, horizontal-axis wind turbine rotor diameter, thrust coefficient, turbine efficiency, air density, and wake expansion rate to estimate location of wind-based generators. These locations are optimized for maximum output power, and minimum deployment costs, which can be estimated for multiple farm areas. This text also proposes various case studies and hypothesis about these deployments, and showcases performance optimizations w.r.t. optimum placement for a given number of turbines. Based on this evaluation, it was observed that the proposed GRMWBO model is capable of improving power efficiency by 18.5%, reduce cost by 8.3%, and reduce number of wind-based generators by 2.9% when compared with various state-of-the-art models. It was also observed that the proposed model showcased consistent performance under different use cases. Thus, making it applicable for a wide variety of real-time deployments.

CFD–DEM Simulation of Dust Deposition on Solar Panels for Desert Railways

With the greening of the railway energy supply chain, large-scale photovoltaic power stations will be the best choice to integrate with the railways. Understanding the deposition mechanisms and rules of dust grains on photovoltaic panels is of great guiding significance for the operation of photovoltaic (PV) power stations. In this paper, based on computational fluid dynamics (CFD) combined with the discrete element method (DEM), the dynamic dust deposition process on solar panels was simulated, and the flow field around solar panels and the movement of dust particles in the wind were calculated. The simulation results of clay particles (d = 10 mm) and fine sand particles (d = 100 mm) under different wind speeds showed that the clay particles could follow the air flow properly, and their deposition rate was only 4.6%, while the deposition rate of the fine sand particles was up to 32%, which was determined by the inflow wind speed and cohesion parameters. An image of the non-uniform distribution of particles on the panels was given in this paper for the first time. This will provide a basis for a more accurate assessment of the impact of dust accumulation on PV output in real-world environments. These results provide a critical reference for railway photovoltaic power supply development in desertification areas.