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Duo to fluctuations in atmospheric turbulence and yaw control strategies, wind turbines are often in a yaw state. To predict the far wake velocity field of wind turbines quickly and accurately, a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions. To consider the shear effect of the vertical incoming wind direction, a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion. This work also developed a "prediction-correction" method to solve the wake velocity field, and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine. Moreover, a 33-kW two-blade horizontal axis wind turbine was simulated using this method, and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics (CFD) simulation results. The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field. Finally, computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8% compared to the experimental values. The established wake model is less computationally intensive than other methods, has a faster calculation speed, and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines. [ABSTRACT FROM AUTHOR]

In this paper, a supervisory control concept for wind farms is proposed based on the neighboring wind turbines control functions in localized areas for power optimization considering wake effects. The flow control in wind farms to maximize power production is a challenging problem due to its time-varying nonlinear wake dynamics. Hence, we develop a method that authorizes coordination in a wind farm for a squarely payoff-based scenario where the turbines have access only to measurements from their neighbors via repeated interactions. Therefore, in order to maximize output power in a wind farm, an Adaptive Learning Game Theory (ALGT) method is introduced. This control scheme provides an interaction framework that constructs a series of common control functions. Here, in every iteration, each turbine chooses an independent decision according to a localized control law. The control objective of wind turbine i determines how each turbine adjusts a decision at each iteration by processing available information. [ABSTRACT FROM AUTHOR]

Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators. [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]

To decrease the power deficit of a wind farm caused by wake effects, the layout optimization is a feasible way for the wind farm design stage. A suitable optimization algorithm can significantly improve the quality and efficiency of the optimization process. For exploring the high-performance algorithms under different layout densities, a comparison is conducted by optimizing the layout of a real offshore wind farm with five algorithms, namely two population-based algorithms and three single-point algorithms. Wake effects are considered by a full-field wake model. A penalty function is proposed for the population-based algorithms to handle the constraint violations. Different iterations and constraints of the layout density are applied in the optimization. The random search has the best optimization results in all the cases and the control of the feasibility check is necessary for this algorithm. More iterations can advance the optimization results. The density constraint greatly affects the computational cost of the random search, which is significantly increased under the strict constraint. Except under the strict constraint, the random search has the best performance of optimization efficiency. A combination of the pattern search and random search is recommended when the strict constraint is applied in the layout optimization. [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]

The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage. [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 objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have been evaluated and compared to observational data, and also with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of 10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively. [ABSTRACT FROM AUTHOR]

In the wind industry, power prediction of wind farm is commonly implemented by analytical wake models, which is low-cost but insufficient in accuracy for high-turbulent wake modelling. In this study, a novel machine-learning-based wake model is developed to improve the power prediction of wind farms. The presented model can reproduce the velocity and turbulence fields in turbine wakes commensurate to the high-fidelity Computational Fluid Dynamics (CFD) simulations while achieving good computational efficiency. Driven by massive CFD simulation dataset, the implicit relationship between inflows and wake flows is established using the Artificial Neural Networks (ANN) technique based on back-propagation algorithm. The reduced-order method Actuator Disk Model with Rotation (ADM-R) and modified k − ε turbulence model are implemented into RANS simulations to save the computational costs dramatically in producing the big-data of wake flows. The ANN wake model is deployed in the Horn Rev wind farm, and validated against LES, onsite measurement, and analytical wake models. The conclusions show that the ANN model can appreciably improve the power predictions compared with the existing analytical models and match the LES and measurement data well. The validated model is also adopted to investigate the influence of wind direction and turbine layout on power production of wind farms. • The machine learning-based wake model is competitive in both efficiency and accuracy for wind farm power prediction. • Turbulence modeling dominates the recovery speed of turbine wake, which is critical for accurate power prediction. • Comprehensive predictions of power output and losses inside Horns Rev wind farm under various wind directions are discussed. • In the Horns Rev wind farm, aligned layout performs better than staggered layout due to the local dominated wind directions. [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]

This paper presents a data-driven wind farm layout optimization framework that uses a machine learning wake model that considers physical control stages. The machine learning wake model is trained using well-validated Computational Fluid Dynamics (CFD) simulation data, and consists of thousands of sub-models, each of which is an artificial neural network (ANN) wake model. The ANN wake models are trained separately for low-speed and high-speed inflows to ensure high accuracy of the predictions, with less than 2% error compared to CFD simulations. The accuracy and efficiency of the framework are validated, and the results show better agreement with CFD simulation than an analytical wake model developed in recent years. A parametric study on the number of wind turbines in the Horns Rev wind farm demonstrates that more wind turbines can be added with a minor decrease in average power, with more even and staggered layouts. Under full-wake uniform inflow, the selected analytical wake model exhibits a power prediction error of 5%–8%, while the differences between optimal layouts searched by different wake models range from 2% to 8%. When introducing a wider range of inflow direction sectors, the discrepancy between optimal layout performances decreases. [ABSTRACT FROM AUTHOR]

• A novel method based on the characteristics of all commercial turbines is proposed. • The self-organizing map is a powerful means to perform a comprehensive analysis. • Maximising capacity factor and minimising levelized cost of energy generally match. • Installed power or rated speeds should be reduced, while rotor diameters maximised. • Mere maximisation of wind farm energy production is a misleading target. A novel method was developed to detect the optimal onshore wind farm layout driven by the characteristics of all commercially-available wind turbines. A huge number of turbine combinations (577) was processed, resulting in 22,721 generated layouts. Various assumptions and constraints were considered, mostly derived from the literature, including site features, wind conditions, and layout design. For the latter, an irregularly staggered turbine array configuration was assumed. Wake effects were simulated through the Jensen's model, while a typical turbine thrust coefficient curve as a function of wind speed was originally developed. A detailed cost model was used, with levelized cost of energy selected as primary and capacity factor as secondary objective function. The self-organizing maps were used to address a thorough analysis, proving to be a powerful means to straightforwardly achieve a comprehensive pattern of wind farm layout optimization. In general, the two optimization functions basically match, while for higher wind potential sites, increasing capacity factor did not necessarily result in decreasing levelized cost of energy. The latter may be minimised by reducing the total number of turbines or the overall wind farm capacity, as well as maximising rotor diameters or minimising rated wind speeds; increasing rated power or hub height is only beneficial for mid-potential sites. The mere maximisation of wind farm energy production is a misleading target, as corresponding to mid-to-high values of levelized cost of energy. In contrast to previous studies, the use of turbines with different rated power, rotor diameter or hub height should be avoided. [ABSTRACT FROM AUTHOR]

One of the current major challenges in wind energy is to maximize energy production of wind farms. One approach in this effort is through control of wind turbine wake interactions, since undesirable wake interactions can introduce additional mechanical stresses on turbines, leading to early failures and reduce overall energy production of wind farms. To develop control strategies that can minimize wake interactions, it is essential to simulate wake behaviors accurately and quickly. In this work, a fast and accurate turbine wake model capable of modeling turbine wakes under yaw is presented. This model builds upon the work of existing wake models and is capable of producing results comparable to that of conventional full CFD simulations using a fraction of the computational cost. The accuracy and speed of the proposed model allows for the development of real-time turbine control strategies to maximize power output. The results of the proposed model are validated with previous numerical and experimental data. • A fast and accurate wake model for simulating wakes of yawed turbines is proposed. • The accuracy of the model has been validated with full CFD simulations and experimental data. • The model solves at least 2 orders magnitude faster than full CFD simulations. [ABSTRACT FROM AUTHOR]

In order to study the effect of vertical staggering in large wind farms, large eddy simulations (LES) of large wind farms with a regular turbine layout aligned with the given wind direction were conducted. In the simulations, we varied the hub heights of consecutive downstream rows to create vertically staggered wind farms. We analysed the effect of streamwise and spanwise turbine spacing, the wind farm layout, the turbine rotor diameter, and hub height difference between consecutive downstream turbine rows on the average power output. We find that vertical staggering significantly increases the power production in the entrance region of large wind farms and is more effective when the streamwise turbine spacing and turbine diameter are smaller. Surprisingly, vertical staggering does not significantly improve the power production in the fully developed regime of the wind farm. The reason is that the downward vertical kinetic energy flux, which brings high velocity fluid from above the wind farm towards the hub height plane, does not increase due to vertical staggering. Thus, the shorter wind turbines are effectively sheltered from the atmospheric flow above the wind farm that supplies the energy, which limits the benefit of vertical staggering. In some cases, a vertically staggered wind farm even produced less power than the corresponding non vertically staggered reference wind farm. In such cases, the production of shorter turbines is significantly negatively impacted while the production of the taller turbine is only increased marginally. [ABSTRACT FROM AUTHOR]

To further understand the spatiotemporal inhomogeneity characteristics of wake profile caused by the inflow change of upstream wind turbine and reduce the influence of wake expansion on the power output and fatigue load of downstream wind turbine, a three-dimensional full wake model (3DJGF model) and a three-dimensional temporal wake model (3DJGF-T model) are proposed and verified in this paper. Assuming that the downstream wake distribution of wind turbine is double-Gaussian distribution, a new 3DJGF model is derived based on the law of conservation of mass and considering the wind shear effect. Based on the 3DJGF model, the time variable is introduced to calculate the delay time, and the 3DJGF-T model of the whole flow field is obtained. The accuracy of the proposed 3DJGF model and 3DJGF-T model is verified by wind field measurement and wind tunnel test, and the relative error of the 3DJGF-T model is analyzed: under the time-varying characteristics of different incoming wind speeds, the error of the newly proposed 3DJGF-T model is basically within 4%. The results show that the 3DJGF-T model can effectively describe the spatiotemporal inhomogeneity distribution and expansion characteristics of wake, which can provide a reference for the dynamic load analysis of downstream wind turbine considering the dynamic adjustment process of wind turbine control strategy, further improve the wake model and optimize the control strategy of wind turbine. • The spatiotemporal inhomogeneity of wake profile is proposed. • The delay time of the wake expansion is defined and considered. • 3D full wake model and 3D temporal wake model are proposed. • Both field measurement in wind farm and wind tunnel test are conducted. • The accuracy of 3D full wake model and 3D temporal wake model is verified. [ABSTRACT FROM AUTHOR]

By optimizing the positions of wind turbines in a wind farm, the power loss caused by wake effects can be reduced maximally. A new methodology of layout optimization is proposed utilizing a full-field wake model that integrates the near-field and far-field wake models after modifications, and a random search (RS) algorithm improved with a scale factor for acceleration in high-density layouts. The methodology is applied to a floating wind farm composed of modular platforms, which have a novel configuration and the ability to face toward the wind direction. The applicability and efficiency of the methodology and the improved RS algorithm are validated. The power production of optimized layouts shows a flat crest with an increased number of wind turbines. There is a layout with maximal output power in the wind farm. The real optimal layout should be determined in consideration of both output power and cost. Two sizes of platforms with different number of modules are compared in the application. The wind farm with smaller platforms produces more power. For comparison, a pattern search (PS) algorithm is also implemented in the application. The improved RS algorithm shows outperformance compared with the original RS and the PS algorithm. [ABSTRACT FROM AUTHOR]

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%. [ABSTRACT FROM AUTHOR]

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system's power output as a result of the wake effect. For any wind farm, it is evident that wind turbines' wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha's electricity demand was carried out in order to obtain the district's daily load profile required to size the wind farm's capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators' placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm's capacity, the district's energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year. [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]

Wake models play a fundamental role in finding optimized solutions in wind farm control. In fact, they allow assessing how wakes develop and interact with each other with the agility required for real-time applications. In this paper, a Gaussian Wake Model (GWM) is implemented in the OpenFAST framework in a way such that its fidelity is increased with respect to previously implemented models, while enhancing its compatibility with control purposes. The OpenFAST tool is coupled with Floris, NREL's software based on the GWM, in order to simulate the wake effect on downstream machines (in the case where the downstream rotor is fully covered by the wake, only partially covered by the wake, of the wake is generated by the interaction of more than one turbine), while the rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. We intend this work as a starting point for developing and testing open/closed-loop control logics that will work in real wind farms. To show the suitability of the implementation, the entire model is then compared to Floris. [ABSTRACT FROM AUTHOR]

An appropriate yaw angle misalignment of the wind turbines in a wind farm has been found to improve the average energy production of the turbine array. Predicting the spatial evolution of the yawed turbine wakes is a key factor in optimizing the yaw angles. In this study, a new three-dimensional yawed wake model is proposed to estimate the non-centrosymmetric cross-sectional shape of the yawed wake velocity distribution, and the model is experimentally validated. Then, a yaw angle optimization strategy that optimizes the wind farm yaw angle distribution and maximizes the power output using the proposed wake model is described. The covariance matrix adaptation evolution strategy is employed as an intelligent algorithm to implement the optimization. The results indicate that yaw angle optimization improves the power of an offshore wind farm by up to 7%, and the optimization yields better results for a small streamwise spacing between turbines than for a large streamwise spacing. Wind farm yaw angle optimization shows great promise for the development of smart wind farms because it has the potential to enable real-time optimization of the yaw angles in response to changes in the incoming wind direction. • A new 3-D wake model is proposed to predict the yawed turbine wake velocity. • The wake model can predict the curled cross-section shape of the wake velocity. • The yaw angle misalignment is adopted to improve the wind farm power production. • An optimization strategy is proposed to adjust the wind farm yaw angles. • The optimization strategy is based on the wake model and an intelligent algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a review on wind farm (WF) layout optimization with multiple types of wind turbines (WTs). Compared with uniform WF layout optimization, the three key research points of this topic are WT type selection, wake modelling and optimization algorithm design. Firstly, a series of WT power curves are demonstrated with a WT type selection method to choose the most suitable ones. When calculating the WF power output, a one-dimensional (1D) and a two-dimensional (2D) wake models are briefly introduced and a newly-developed three-dimensional (3D) wake model is described in detail for the application in the nonuniform WF layout optimization. The objective functions, constraints and optimization algorithms used in the literature are reviewed and the optimization framework is built. Case studies are carried out on two real-world WFs. One is the Greater Gabbard offshore WF, on a flat area with an irregular shape and the other is Huade II onshore WF, on a mountainous area with a square shape. The aim of this paper is to shed light on the most significant aspects in the nonuniform WF optimization design based on the summary of the latest works. In addition, future works have been pointed out in the conclusion. • Optimization frameworks of nonuniform wind farm design are reviewed. • A three-dimensional wake model is used in the wind farm nonuniform optimization. • Two real wind farms are optimized with mixed-installation of wind turbines. • Wind farms are suggested to be designed with multiple types of wind turbines. [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]

• A new engineering wake model for wind farm in complex terrain is proposed. • The new model yields accurate results comparable to CFD simulation and is fast to run. • Systematic comparison of eight algorithms is done for wind farm layout optimization. • Random search, local search and pattern search are found to be the best algorithms. Layout optimization of wind farms constitutes an important and challenging task in complex terrain. This is especially due to the complex interactions of the boundary layer flows in complex terrain and wind turbine wakes, which renders wake modelling in complex terrain difficult. This study tackles this challenge with a new engineering wake model, which is developed by superposing a Gaussian shape wake model on top of the background flow field, assuming that the centerlines of wind turbine wakes follow the streamlines of the background flow field. The model is found to predict wind turbine wakes in complex terrain with good accuracy and at the same time it is computationally cheap to run for optimization applications. Comparisons with high fidelity simulations and field measurements for a real wind farm with 25 turbines in complex terrain demonstrate its effectiveness. A systematic comparison of eight optimization algorithms, which includes two gradient-based and six gradient-free algorithms, is also carried out for the layout optimization problem in complex terrain. To accelerate the optimization process, a double-stage approach is proposed, which optimizes the objective function first neglecting wake effects and then, in the second stage, including them. While all the tested optimization algorithms can improve the original wind farm layout, random search, local search, and pattern search are found to be the top three algorithms in terms of optimization results and computational cost. [ABSTRACT FROM AUTHOR]