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In this article, we present an investigative study on the often-overlooked partial wake phenomenon in previous studies concerning wind farm configurations. A partial wake occurs when a portion of the actuator disk of a downstream wind turbine is affected by the wake of another upstream turbine. This phenomenon occurs in addition to the full wake, where the entire upstream turbine is affected by the wake of the frontal turbine, also leading to a decrease in wind speed and consequently a reduction in power production. The proposed study is based on measuring the power generated by the area swept by the wake of an array of turbines in a wind farm. To accomplish this, we integrate the linear wake model of Jensen, the specifications of the ENERCON E2 wind turbine, and the wind farm data into Matlab-developed software (version 18) to perform the calculations. In a concrete application, this proposed method is validated by reproducing the previous works that neglected the partial wake in wind farm configurations. The simulation results obtained are analyzed, compared, and discussed under similar operational conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine's optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine's internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of three wind farm simulators, examining the influence of wake on the local wind speed and power output for downstream turbines using experimental data. The study features experiments in three distinct scenarios, evaluating differences among the simulators by calculating the local wind speed and power for each. Each simulator employs a unique wake model, which substantially affects the local wind speed experienced by downstream turbines. Furthermore, the experiment involves adjusting parameter values for each simulator to assess their respective impacts on wind farm performance. The findings of this research are expected to play an important role in investigations related to power optimization and wake effects in the wind farm control. [ABSTRACT FROM AUTHOR]

This paper discusses how the incorporation of high-resolution ground coverage dataset ESA WorldCover into a wind flow field and wake simulation calculation, as well as the use of the coupled wake model for wind farm output simulation, can improve the accuracy of wind resource assessment using engineering examples. In the actual case of grid-connected wind farms in central China, SCADA wind speed data is reconstructed to the free flow wind speed in front of the wind turbine impeller using the transfer function of the nacelle, and the wind farm is modeled using OpenWind software, simulating the wind speed at the height of each wind turbine hub and each wind turbine output. The results show that when other initial data are consistent, using ESA's high-precision land cover dataset WorldCover 10 m to make roughness lengths which improves the wind farm output simulation accuracy by 8.91%, showing that it is worth trying to apply WorldCover 10 m to the wind farm simulation design. At the same time, this case is used to compare and analyze the application of the Eddy-Viscosity wake model and the two coupled wake models based on the Eddy-Viscosity wake model. The results show that the coupled wake model will have higher accuracy than the Deep Array Eddy Viscosity wake model and it is 1.24% more accurate than the Eddy Viscosity wake model, and the ASM Eddy Viscosity wake model is 5.21% more accurate than the Eddy Viscosity wake model. [ABSTRACT FROM AUTHOR]

This study employs a UAV anemometry system to assess the wind field around a yawed wind turbine, particularly focusing on its wake during operational conditions. The research findings reveal that the evolution of wind turbine wakes follows distinct patterns at various downstream distances. Turbulence intensity notably amplifies within regions characterized by significant fluctuations in mean wind speed. Specifically, in yawed conditions, the areas with the highest turbulence generated by the rotor coincide with zones exhibiting pronounced variations in mean wind speed. The heightened turbulence within the wake region to some extent constrains the safety and economic viability of wind farms. Turbulence intensity increases significantly in the region where the average wind speed changes greatly, that is, under the yaw state of the wind turbine, this region is characterized with the strongest turbulence generated by the rotor. The UAV anemometry system's wind speed assessment closely matches predictions from the Y-3DJGF model, accounting for wake experience coefficient adjustments. Furthermore, in yaw conditions, the wind turbine's wake trajectory exhibits some deviation from the incident flow's direction, with an initial increase in slope followed by gradual stabilization. As the downstream distance increases, the trajectory will eventually establish a consistent trend with the incident flow. The cost-effective and flexible UAV anemometry system enhances wind field measurements, offering an innovative approach for wind energy sector research and engineering. • Development of a UAV-based anemometry system for wake measurement of yawed wind turbines. • Investigation of wake characteristics under yawed conditions, revealing significant reductions in wake wind speed and increased turbulence intensity, especially at 2D downstream. • Experimental validation of wake trajectory deviations under yaw conditions, highlighting changes in slope and eventual alignment with incident flow downstream. • Validation of wind speed measurements using the UAV anemometry system against predicted results from the Y-3DJGF model. • Application of the UAV anemometry system in wind resource assessment, layout optimization, and power generation efficiency forecasting for wind farms. [ABSTRACT FROM AUTHOR]

The operational security of power systems for wind farm (WF) integration becomes more important as the penetration of wind generation increases. A WF may contain hundreds of wind turbines (WT) and thus the power system simulations based on the complex model of a WT consume much time. This article proposes an equivalent WF model with the frequency-regulation function. The main research works include the establishment of the generic model of a WT, the development of single- and multimachine equivalent models for a large WF, the clustering of WTs based on the developed nonlinear wake model and the fuzzy C-means (FCM) clustering algorithm, the integration of the frequency-regulation function into the equivalent WF model, and the validation of the developed equivalent WF model by various operating scenarios. The results demonstrate that the proposed multimachine equivalent method based on the FCM clustering provides a more accurate model than a single-WT equivalent model. Consequently, the research results provide a solution for simulating a large-scale wind power integration, providing efficient, accurate, and fast steady-state and dynamic analyses. [ABSTRACT FROM AUTHOR]

The wake of wind turbine in a complex terrain site is investigated by a modified delayed detached eddy simulation (MDDES) and a hybrid framework to predict the wind turbine wake effects in a real wind farm are presented. The wind field and turbine wake flows over the real terrain are simulated for different directions and validated by comparing with those measured by a LiDAR in the wind farm. Subsequently, the wake characteristics over the escarpment are systematically investigated by numerical simulations and the terrain effects on the turbine wake flow are clarified. A hybrid framework is then utilized for a wind farm over complex terrain. In this framework, the effects of local terrain and surface roughness on the wind speed, wind direction and turbulence intensity are taken into account by the numerical simulation, while the wind turbine wakes are represented by a new Gaussian-based analytical wake model. Finally, the applicability of the proposed framework is verified by comparison with the numerical simulation results of wake over the escarpment. • Numerical simulations on the wind turbine wake over complex terrain are conducted and validated by the field measurement. • The wind turbine wake characteristics over the escarpment with different elevation are systematically investigated. • A hybrid framework is utilized for wind turbine wake prediction over complex terrain and its applicability is verified. [ABSTRACT FROM AUTHOR]

Recent wake studies suggest that vertical axis wind turbines (VAWTs) exhibit a potential increase in packing density in wind farm situations. In this study, an analytical wake model fit for straight-bladed VAWTs is developed and applied to the micro-siting problem of VAWTs. The wake asymmetry of VAWTs in the horizontal direction distinguishes itself from the axisymmetric wake of horizontal axis wind turbines (HAWTs). In the proposed wake model, it was assumed that the wake region at a given distance was encompassed by two semi-ellipses. The velocity in the wake was derived based on continuity conservation. Wake measurements were conducted in a wind tunnel, and the model parameters were derived by fitting predicted results to parameters measured in experiments. The wake model was next used to evaluate the park effect. Due to the asymmetry, a method to determine the position of a turbine relative to its upstream counterparts was developed. To faithfully reflect the wind conditions on sites, distributions of both wind direction and speed were considered. The layout design of VAWTs was first performed on regular wind farm sites. Moreover, the cluster configuration was generalized to irregular wind farms. The winding number method was adapted specifically for the irregular constraints. [ABSTRACT FROM AUTHOR]

The modeling of wake effects plays an essential role in wind farm optimal design and operation. In this study, a novel deep learning method, called Super-Fidelity Network (SFNet), is proposed for wind farm wake modeling, which would be the first attempt to combine the advantages of both analytical models and numerical models through deep learning methods. Specifically, the low-fidelity flow fields generated by the analytical models serve as the prior information for predicting high-fidelity flow fields. Then the SFNet learns the mapping relationships between low-fidelity data and high-fidelity data, thereby predicting high-fidelity flow fields without resorting to huge computational resources. Numerical experiments demonstrate that the mean absolute error of the developed model is just 1.9% with respect to the freestream wind speed when compared with high-fidelity data, after trained on only 45 samples. In addition, the generalizability of the proposed SFNet in yaw angles, wind speeds and array column extensions is verified by a series of numerical experiments. Furthermore, the experimental results demonstrate that the trained model is able to predict the flow field of a wind farm consisting of 100 turbines within several seconds based on a standard desktop. [Display omitted] • A novel deep learning method is proposed for wind farm wake modeling. • The advantages of analytical and numerical models are combined by our method. • The developed model can achieve accurate and robust wake predictions. • The generalizability of the proposed method is widely verified. • A case study based on a 10 × 10 wind farm is carried out by the developed model. [ABSTRACT FROM AUTHOR]

A wind turbine operating in the wake of another turbine and has a reduced power production because of a lower wind speed after rotor. The flow field in the wake behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. Therefore modeling wake effect is necessary because it has a great influence on the actual energy output of a wind farm. In this paper, the extreme learning machine (ELM) coupled with wavelet transform (ELM-WAVELET) is used for the prediction of wind turbine wake effect in wind far. Estimation and prediction results of ELM-WAVELET model are compared with the ELM, genetic programming (GP), support vector machine (SVM) and artificial neural network (ANN) models. The following error and correlation functions are applied to evaluate the proposed models: Root Mean Square Error (RMSE), Coefficient of Determination ( R 2 ) and Pearson coefficient ( r ). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by ELM-WAVELET approach (RMSE = 0.269) in comparison with the ELM (RMSE = 0.27), SVM (RMSE = 0.432), ANN (RMSE = 0.432) and GP model (RMSE = 0.433). [ABSTRACT FROM AUTHOR]

The grouping of turbines in large farms introduces that a wind turbine operating in the wake of another turbine and has a reduced power production because of a lower wind speed after rotor. The flow field in the wake behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. Consequently, the downstream turbines in a wind farm cannot extract as much power from the wind as the first row turbines. Therefore modeling wake effect is necessary because it has a great influence on the actual energy output of a wind farm. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate wake effect in a wind farm according to wind turbine positions in wind farm, distances between turbines in the wind farm and rotor radius as well. [ABSTRACT FROM AUTHOR]

Production losses and increased turbine loadings are observed in wind farms, when wind turbines interact with each other. If a wind turbine is located in the wake of another one, its incoming flow is disturbed, slowed down, and its potential wind power is decreased. It is therefore necessary to study the wind turbine wakes and their interactions. It is important to consider these wake effects in the design of a wind farm in order to maximize the energy output and lifetime of the machines. The exact modeling of the wind speed distribution within a wind park is a fairly complicated task and many of the necessary parameters are not routinely available. A large number of studies have been established concerning the calculation of wake effect. Even though a number of mathematical functions have been proposed, there are still disadvantages of the models like very demanding in terms of calculation time. Artificial neural networks (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the wake power deficit. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Reducing wake losses and improving the overall power output of wind farms have become a research focus in attempts to optimize wind farm power generation. A cooperative multiagent optimization method (CMAOM) for wind farm power delivery maximization has been proposed in this paper. In the CMAOM, a wind farm wake distribution calculation model, based on the Jensen wake model, was constructed, and each turbine was then assigned as an agent; the CMAOM was used to reduce wake losses and improve the overall wind farm power output. The agent, multiagent objective function and grid environment were defined in this study using wind turbine characteristics, and the CMAOM, including the neighborhood competition operator, mutation operator, and self-learning operator, were calibrated using wind turbine aerodynamic correlation characteristics. The Danish Horns Rev wind farm was selected as a case study, and the CMAOM and particle swarm optimization (PSO) algorithm were used to conduct analyses there. The results showed that the CMAOM proposed in this paper was more effective than the PSO algorithm and that the wind farm overall power output was increased by 7.51% for a 270° incoming wind direction and an incoming wind speed of 8.5 m/s. • A cooperative multiagent optimization method was proposed for power maximization. • Agent, objective function and grid environment were defined. • Competition operator, mutation operator, and self-learning operator, were calibrated. • A wind farm wake calculation model, based on the Jensen wake model, was constructed. [ABSTRACT FROM AUTHOR]

The arrangement of wind turbines in clusters presents two noteworthy issues: (1) diminished power generation brought about by wake wind speed deficits and (2) expanded unique loads on the blades caused by higher turbulence levels. The drop in power generation of downstream wind turbines can reach up to 46% of the upstream wind turbines in entirely created wake conditions. Thus, wind farm layout optimisation is an essential design criterion of a wind farm. Focusing on optimising the micro-position of the turbines within the wind farm to minimise the wake effects can maximise the expected power output. There are several crucial aspects in designing a wind farm layout, and most of the previous studies have been focusing on maximising the overall energy yield and minimise the initial investment. Several optimisation methods based on heuristic techniques have successfully found optimal solution to these challenges. However, far too little attention has been paid on comparing these methodologies to each other. Therefore, this work aims to survey the existing approaches used to-date in solving the wind turbine positioning within the wind farm. This work firstly briefs the historical background of the conventional development of a wind farm. Then, key features and basic components of a wind farm modelling are discussed and categorised into the following: wind farm siting, layout design, wake models and objective functions. Several research gaps and suggestion of future research areas in this field are highlighted which shall enable to guide researchers to explore further in the upcoming studies. • Review the recent approaches in wind turbine arrangement optimisation. • Highlight major challenges on wind turbine micrositing optimisation. • Artificial intelligence integration as promising optimisation strategies. • Focus review on three wind scenarios as performance benchmarking measurement. [ABSTRACT FROM AUTHOR]

• A parallel framework for wind farm optimization is developed using six wake models. • Framework accounts for terrain elevation and wake-terrain interaction. • Four turbine specific cost models are developed. • Wind farm area model is developed as the area of convex hull. • Optimization of a realistic wind farm is performed for maximum power generation. A new framework for Wind Farm Layout Optimization (WindFLO) is developed to accelerate the design of wind farms. The framework provides a large set of analytical wake models and wake superposition schemes. It is able to take into account terrain elevation and the ambient wind velocity profile. The schemes in the WindFLO model were validated against experimental data from a wind tunnel to within 1% relative error. A turbine rotor diameter and height dependent cost model was also developed using data from 250 different wind turbines. A land usage model was also developed using the convex hull approach. The framework was used to optimize a wind farm layout for maximum annual energy production using real wind farm terrain and conditions. The nonlinear optimization problem was solved using a robust Single-Objective Hybrid Optimizer. The wind farm layout and wind turbine (rotor diameter and tower height) were optimized and resulted in increased annual energy production, reduced cost and reduced land usage. The WindFLO framework is made publicly available to accelerate and advance the techniques for wind farm optimization. 1 1 WindFLO can be downloaded from https://github.com/sohailrreddy/WindFLO. [ABSTRACT FROM AUTHOR]

• The machine learning framework achieves favorable accuracy and efficiency in the wake prediction. • ADM-R/RANS coupling model can efficiently produce big wake datasets for machine learning model. • For an aligned turbine row, power drops at the second turbine and gradually stabilizes afterwards. • Turbulence model plays an important role in accurate power prediction of wind farm. In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the back-propagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k - ε turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2 MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANN-based wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions. [ABSTRACT FROM AUTHOR]