Wind turbine wakes modeling and applications: Past, present, and future

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

The wake effect due to aerodynamic interaction among wind turbines makes the wind farm control a challenging problem. The issues on the wind farm control considering the wake effect include the power generation control, reduction of structural loads, and maximization of energy capture. Among these issues, this paper focuses on the maximization of energy capture and proposes an iterative sequential optimization method (ISOM). The ISOM is not limited to a particular wake model and is based on the actual measurement of the generated power. The ISOM aims to reduce the number of iterations by exploiting the properties of the maximization problem, which include the iterative single-variable optimizations and the sequential optimization in the direction from upwind turbines to downwind ones. Further, the ISOM achieves a significant level of performance in terms of the generated power at the early stage of the iteration process. The effectiveness of the ISOM is verified by the simulations using a four-turbine wind farm and the Horns Rev wind farm. The results show that the ISOM can effectively search the optimal solution with the trade-off between the number of iterations and the precision of the solution.

Wake-effect Minimising Optimal Control of Wind Farms, with Load Reduction

The clustering of wind turbines within a prospective site is prone to significant wake interactions, which have a negative impact in terms of loads and plant performance. Further improvements in the design and control of wind farms can be achieved through a detailed assessment of wake effects under site-specific conditions. The aim of this paper was to investigate wake effects in atmospheric boundary layer (ABL) flows using a simplified model wind turbine. First, we performed experiments under smooth- and turbulent-uniform inflows to highlight the wake features of the model. Second, we evaluated the interactions with two different neutrally stratified ABL: i) ABL Type-I (α = 0.11, TI = 6 %), ii) ABL Type-II (α = 0.23, TI = 13 %), where α is the incident wind shear and TI is the turbulence intensity. For each case, hot-wire anemometry was used to obtain wake mean velocity, turbulence intensity and power spectrum profiles at four different downstream positions. We also included a comparison with three analytical wake models to get further indications on the experimental measurements. First results showed that the simplified model is well-suited for its intended purpose. The largest discrepancies between experiments and model predictions were in the near-wake region and in cases with lower background turbulence. We also corroborated a strong influence of the incoming wind profile on how fast the wake recovers to the undisturbed conditions. Furthermore, this study provided an initial framework to explore the ABL wind tunnel at Universidade Federal do Rio Grande do Sul (UFRGS) as a viable tool for research in wind energy.

The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single‐objective and multi‐objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system.

Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines

Critical evaluation of Wind Turbines’ analytical wake models

The creation of wakes, with unique turbulence characteristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.

Abstract. Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

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.

This paper analyses, from a steady state point of view, the potential benefit of a wind farm control strategy whose main objective is to maximize its total energy yield over its lifetime. Unlike the conventional approach in which each wind turbine operation is optimized individually to maximize its own energy capture, the proposed control strategy aims to optimize the whole system by operating some wind turbines at suboptimum points, so that the wake effect within the wind farm is reduced and therefore the total power generation is maximized. The methodology used to assess the performance of both control approaches is presented and applied to a particular study case consisting of three wind turbines. The paper contains a comprehensive wake model considering single, partial and multiple wake effects. The study also takes into account the Blade element theory and actuator disc theory to accurately compute both power and thrust coefficient of each wind turbine. The results suggest a good potential of the proposed concept, since an increase in the annual energy captured by the wind farm from 0.3% up to 3.7% may be achieved (depending on the wind rose at the wind farm location).

Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

This paper presents a bi-level planning approach to find an optimal layout for a large-scale wind farm (WF) from economic and reliability perspectives. The upper level of the planning approach is formulated as constrained multi-objective optimization (MOO) to obtain the optimal WF layout, where two conflicting objectives are optimized simultaneously. Two objectives for MOO are i) yearly gross energy production and ii) annual loan repayment (for cable and land area of the WF), where the first objective is maximized and the second objective is minimized. Jensen's wake model is used to calculate the wake effect, and to evaluate the multiple wake effect within a WF, a coordinate-transformation-based approach has been adopted in this study. An efficient hybrid multi-objective metaheuristic algorithm, which is based on nondominated sorting multi-objective particle swarm optimization and improved archive multi-objective simulated annealing, has been proposed to find the optimized WF layout. The superiority of the Pareto front obtained by the proposed algorithm for the large-scale WF layout is supported by its comparison with different powerful and well-established MOO algorithms. In the lower level of the proposed approach, economic and reliability analyses of the solutions (WF layout), obtained in the Pareto front by the proposed hybrid algorithm, have been done. In this context, two performance metrics, a) generation economy index and b) expected wind energy generation (EWEG), have been considered. A sequential Monte Carlo simulation has been developed to calculate the EWEG considering random failure of both wind turbines and cables in this paper. The most suitable WF layout is selected by trading off between economy and reliability using a fuzzy decision-making approach.

In the wind turbine wake control research, the analytical wake model does not consider the influence of direct control parameters on the wind turbine wake flow field, and its prediction accuracy is by no means satisfying. The numerical simulation method can accurately predict the wake effect, but its low computational efficiency renders it unable to be used in the iterative control optimization study. In this paper, a new wake model coupled with the direct control parameters (tip speed ratio and pitch angle) of a single wind turbine is built based on the machine learning algorithm for which the widely applied Artificial Neural Network (ANN) is applied for instance. By combining the ANN wake model with different wake superposition models, the best combinatorial model for quantitative evaluation of multiple wake interferences is achieved, which not only has great accuracy but also ensures high calculation efficiency. On top of the established combinatorial wake model, three in-line wind turbines are investigated for assessing the effectiveness of wake control optimization with respect to the traditional greedy control. The results show that by optimizing the control parameters (tip speed ratio and pitch angle) of wind turbines, the total power of the three wind turbine units is increased by 1.15~3.48% depending on the incoming wind speeds and distances apart between wind turbines. The power variation of each wind turbine shows that the increase in total power is achieved by the decreased power of the first wind turbine and the increased power of the second and third wind turbines. The research verifies the effectiveness of adopting optimized wind turbine wake control on the total wind farm power improvement numerically.