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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]

This paper describes a fast, accurate, fully unsteady aerodynamic prediction scheme for the calculation of horizontal axis wind turbine performance in yawed flow. The method couples a prescribed wake vortex model with a semi-empirical unsteady aerofoil model to provide the unsteady aerodynamic response of the blades owing to the cyclic variation of incidence. Predictions from the model are shown to compare well with experiment, both in terms of gross performance prediction and detailed blade loads. Finally, the potential for future improvements to the predictive capability of the model is discussed. [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]

The paper will review the development and application of the mathematical modelling of the advanced rotorcraft configuration, including compound helicopter configurations and tilt-rotor vehicles. The mathematical model is the basis for the design of the flight control system and an essential tool to assess the flying and handling qualities for helicopters. As the helicopter is a multi-body system, the mathematical modelling of helicopter should consider the coupling effects among motion, inertia, structure, and aerodynamics, as well as the unsteady and nonlinear characteristics, to give the physical principles and mathematical expression of each part. Therefore, the mathematical modelling of a helicopter is a process of analysing and synthesizing different hypotheses and subsystem models. Moreover, the advanced helicopter configuration puts forward higher requirements for the helicopter mathematical modelling in terms of the aerodynamic interference, blade motion characteristics, and manoeuvre assessment. The critical issues of helicopter modelling, especially the modelling of the advanced rotorcraft configurations, will be illustrated in this paper. The emphasis is put on the modelling of rotor aerodynamics and aerodynamic interaction among the rotor, fuselage, and other parts. Integrated modelling methods and the manoeuvrability investigation are also the foci of the paper. Suggestions for future research on helicopter flight dynamics modelling are also provided. [ABSTRACT FROM AUTHOR]

Oscillating wave surge converters (OWSCs) have been extensively used to exploit wave energy. To maximize the energy output from an OWSC array, layout optimizations must be conducted; therefore, an analytical wake model of OWSC is needed for fast optimization iterations. However, no wake model of OWSC and no wake-model-based array optimization approach have been developed. To this end, this study proposed a novel analytical wake model of OWSC. Based on the proposed wake model and differential evolution algorithm, layout optimizations of OWSCs in wave energy farms were performed. To establish a database for deriving the analytical wake model, OWSCs were numerically simulated using the smoothed particle hydrodynamics (SPH) method, where the incoming wave height, damping of the power take-off system, and flap width were varied. The attained analytical wake model could predict the wave height changes in wake regions of OWSCs with an accuracy of 90.0% compared with the numerical SPH results. The optimized OWSC layouts demonstrated that staggered layouts with narrow and wide OWSCs placed in the front and back of wave energy farms, respectively, were economical and appropriate. Furthermore, a simplified guideline was proposed to determine OWSC layouts. Finally, all the codes implemented in this study were open-sourced. • Novel analytical wake model of OWSC wave energy converter is proposed. • Wave energy farm with OWSCs is optimized based on the proposed analytical wake model. • Simple guideline for determining layouts of OWSC arrays is proposed. • Staggered layouts with narrow OWSCs placed in the front of the farm are recommended. • Self-compiled DE code for OWSC wave energy converter array optimizations is opened. [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]

• 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]

This paper presents a method for reconstructing the wake wind field of a wind turbine based on planar light detection and ranging (LiDAR) scans crossing the wake transversally in the vertical and horizontal directions. Volumetric measurements enable the study of wake characteristics in these two directions. Due to a lack of highly resolved wind speed measurements as reference data, we evaluate the reconstruction in a synthetic environment and determine the reconstruction errors. The wake flow of a multi-megawatt wind turbine is calculated within a 10-min large-eddy simulation (LES) for high-thrust loading conditions. We apply a numerical LiDAR simulator to this wake wind field to achieve realistic one-dimensional velocity data. We perform a nacelle-based set-up with combined plan position indicator and range height indicator scans with eight scanning velocities each. We temporally up-sample the synthetic LiDAR data with a weighted combination of forward- and backward-oriented space–time conversion to retrospectively extract high-resolution wake characteristic dynamics. These dynamics are used to create a dynamic volumetric wake deficit. Finally, we reconstruct the dynamic wake wind field in three spatial dimensions by superposing an ambient wind field with the dynamic volumetric wake deficit. These results demonstrate the feasibility of wake field reconstruction using long-range LiDAR measurements. [ABSTRACT FROM AUTHOR]

CFD simulation with DES-SA turbulence model are conducted to investigate the wake velocity characteristics. The turbine wake velocity distribution of axial, tangential and radial component is analysed by comparing numerical results with previous the experimental and theoretical results. The axial velocity component is continuously recovered along the axial direction. Based on the position of minimum axial velocity, turbine wake can be divided into two zones: zone of flow establishment and zone of established flow. In the zone of flow establishment, two-dipped velocity valleys of the axial velocity distribution appear along the radial direction. These two valleys are combined to one valley at the rotation axis in the zone of established flow. The tangential velocity component is the second largest and the radial velocity component accounts for only a small proportion. Both of tangential and radial velocity decreases along the axial direction. Two velocity peaks of both tangential and radial velocity appear along the radial direction. Several empirical equations of turbine wake are proposed to describe the velocity distribution. • CFD numerical simulation method for tidal current turbine with DES-SA model. • Zone of flow establishment and zone of established flow are divided by the position of minimum axial velocity. • The axial and radial distribution of axial, tangential and radial velocity component are studied based on CFD results. • Empirical equations are proposed to describe the velocity distribution of turbine wake. [ABSTRACT FROM AUTHOR]

In the design of wind energy farms, the loss of power should be seriously considered for the second wind turbine located inside the wake region of the first one. The rotation of the first wind-front rotor generates a high-vorticity wake with turbulence, and a suitable model is required in computational fluid dynamics (CFD) to predict the deficit of energy of the second turbine for the given configuration. A simplified numerical model based on the classical momentum theory is proposed in this study for multiple wind turbines, which is proposed with a couple of tuning parameters applied to Reynolds-averaged Navier-Stokes (RANS) analysis, resulting in a remarkable reduction of computational load compared with advanced methods, such as large eddy simulation (LES) where two parameters reflect on axial and rotational wake motion, simply tuned with the wind-tunnel test and its corresponding LES result. As a lumped parameter for the figure of merit, we regard the normalized efficiency on the kinetic power output of computational domain, which should be directed to maximize for the optimization of wind farms. The parameter surface is plotted in a dimensionless form versus intervals between turbines, and a simple correlation is obtained for a given hub height of 70% diameter and a fixed rotational speed tuned from the experimental data in a wide range. [ABSTRACT FROM AUTHOR]

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms. [ABSTRACT FROM AUTHOR]