Flow Structure and Turbulence in Wind Farms

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.

As wind-turbine arrays continue to be installed and the array size continues to grow, there is an increasing need to represent very large wind-turbine arrays in numerical weather prediction models, for wind-farm optimization, and for environmental assessment. We propose a simple analytical model for boundary-layer flow in fully-developed wind-turbine arrays, based on the concept of sparsely-obstructed shear flows. In describing the vertical distribution of the mean wind speed and shear stress within wind farms, our model estimates the mean kinetic energy harvested from the atmospheric boundary layer, and determines the partitioning between the wind power captured by the wind turbines and that absorbed by the underlying land or water. A length scale based on the turbine geometry, spacing, and performance characteristics, is able to estimate the asymptotic limit for the fully-developed flow through wind-turbine arrays, and thereby determine if the wind-farm flow is fully developed for very large turbine arrays. Our model is validated using data collected in controlled wind-tunnel experiments, and its usefulness for the prediction of wind-farm performance and optimization of turbine-array spacing are described. Our model may also be useful for assessing the extent to which the extraction of wind power affects the land–atmosphere coupling or air–water exchange of momentum, with implications for the transport of heat, moisture, trace gases such as carbon dioxide, methane, and nitrous oxide, and ecologically important oxygen.

In the context of large off-shore wind farms, power production is influenced greatly by the turbine array's interaction with the atmospheric boundary layer. One of the most influencing manifestations of such complex interaction is the increased level of shear stress observed within the farm. This leads to higher momentum fluxes that affect the wind speed at the turbine locations and in the cluster wake. At the wind farm entrance, an internal boundary layer (IBL) grows due to the change in effective roughness imposed by the wind turbines, and for large enough clusters, this can reach the unperturbed boundary layer height in what is referred to as the fully developed regime. Downwind, a second IBL starts growing, while the shear stress profile decays exponentially to its unperturbed state. In the present study, we propose a simple analytical model for the vertical profile of the horizontal shear stress components in the three regions identified above. The model builds upon the top-down model of Meneveau (J. Turbul., vol. 13, 2012, N7), and assumes that the flow develops in a conventionally neutral boundary layer. The proposed parametrization is verified successfully against large-eddy simulations, demonstrating its ability to capture the vertical profile of horizontal shear stress, and its evolution both inside and downwind of the wind farm. Our findings suggest that the developed model can prove extremely useful to enhance the physical grounds on which new classes of coupled wind farm engineering models are based, leading to a better estimation of meso-scale phenomena affecting the power production of large turbine arrays.

As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets.

With increasing implementation of renewable energy, planetary boundary layer (PBL) processes, including the effects of wind farm (WF), are gaining great attention. This research focuses on the dynamic interaction between WF and the PBL using the Weather Research and Forecasting Model based on a large-eddy simulation (WRF-LES), incorporating WF representation via the actuator disc model. A range of stability conditions, from unstable to stable stratification, are examined along with various wind turbine arrangements.The results show a significant effect of WF on increasing turbulent kinetic energy (TKE) above the hub-height level under stably stratified to near-neutrally stratified conditions. In contrast, the effect is not profound in the case of unstable stratification.Three key WF impacts on TKE production can be identified: the effect on mean momentum equation, effect on TKE production due to the WF thrust term, and correction of the length scale. TKE budget analysis reveals that the direct effect of the WF thrust term to TKE production or dissipation is minimal. This implies that the WF effect on TKE production largely depends on the mean-wind profile modulation, increasing shear-production of the TKE. In the case of stably stratified and near-neutrally stratified conditions, the local wind deficits around WT hub height is large. In contrast, the wind speed is entirely decelerated over the boundary layer in case of unstable stratification. This explains why the WF effect is reduced under unstable stratification, where buoyancy-induced mixing dominates over mechanical shear. The length-scale characteristics of the PBL scheme are discussed in terms of how WF modify it.

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

A new multi-fidelity flow-acoustics simulation framework for wind farm application

As wind turbines operate within the atmospheric boundary layer (ABL), the study of their interaction with the ABL flow can help us better understand and predict their performance. In addition to the performance of wind turbines, this interaction has an effect on the flow both upwind and downwind (i.e., wake region) of the turbines. In particular, the study of turbine wakes is of great importance because they are the main cause of power losses and fatigue loads in wind farms. In the current thesis, four studies are conducted to fully examine the turbine interaction with the ABL flow, with an emphasis on turbine wakes. In the first study, a new analytical wake model is proposed and validated to predict the wind velocity distribution in the far-wake region, where downwind turbines usually operate. The proposed model is derived by applying the conservation of mass and momentum and assuming a Gaussian distribution for the velocity deficit in the wake. This simple model only requires one parameter to predict the velocity distribution in the far wake of a wind turbine. In general, it is found that the velocity deficit in the wake predicted by the proposed analytical model is in good agreement with the experimental and numerical data. Furthermore, the results show that the new model predicts the power extracted by downwind wind turbines more accurately than other common analytical models, some of which are based on less accurate assumptions like considering a top-hat shape for the velocity deficit. In the second study, wind tunnel measurements are carried out to systematically investigate turbine wakes under yawed conditions. The detailed experimental data are used to perform a budget study of the continuity and Reynolds-averaged Navier-Stokes equations. This theoretical analysis reveals some notable features of the wakes of yawed turbines, such as the asymmetric distribution of the wake skew angle with respect to the wake center. Under highly yawed conditions, the formation of a counter-rotating vortex pair in the wake cross-section as well as the vertical displacement of the wake center are also shown and analyzed. Finally, this study enables us to develop general governing equations upon which a simple and computationally inexpensive analytical model is built. The proposed model aims at predicting the wake deflection and the far-wake velocity distribution for yawed turbines. The findings of this study can be especially useful to assess the possibility of optimizing wind-farm power production by controlling the yaw angle of the turbines. In the third study, comprehensive wind tunnel experiments are performed to study the interaction of a turbulent boundary layer with a wind turbine operating under different tip-speed ratios and yaw angles. Force and power measurements are performed to characterize the wind turbine performance. Moreover, a high-resolution stereoscopic particle-image velocimetry (S-PIV) system and hot-wire anemometry are used to study the flow in the upwind, near-wake and far-wake regions. This study provides new insights on the turbine and flow characteristics such as the evolution of tip vortices and wake meandering. Finally, the last study concerns the design and the performance analysis of a new three-bladed horizontal-axis miniature wind turbine with a rotor diameter of $15$ cm. Due to its small size, this turbine is particularly suitable for studies of wind farm flows and the interaction of the turbine with an incoming boundary-layer flow. Special emphasis is placed on accurate measurements of the mechanical power extracted by the miniature turbine from the incoming wind. In order to do so, a new setup is developed to measure the torque of the rotor shaft. The thrust and power coefficients of the miniature turbine are found to be around $0.8$ and $0.4$ in optimal conditions, respectively, which are close to the ones of large-scale turbines in the field.

SummaryWind energy has shown a remarkable potential for fulfilling the increasing world's energy demand in a clean and sustainable way. The wind energy industry has installed increasingly sophisticated and larger wind turbines particularly in offshore regions to capture such energy as efficiently and cost effectively as possible. The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis and fault‐tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, this paper uses a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel fault‐tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large offshore wind farm benchmark model in the presence of wind turbulence, measurement noise, load variations, and realistic fault scenarios.

Wind farms with counter-rotating wind turbines

With the increasing integration of the wind power into the power system, wind farm are required to be controlled as a single unit and have all the same control tasks as conventional power plants. The wind farm central controller receives control orders from Transmission System Operator (TSO), then dispatch the wind power reference to each wind turbine. One of the most commonly used dispatch methods is to dispatch the wind power reference to each wind turbine proportional to each wind turbine's available wind power without the consideration of the wake effect. The wake which depends on the thrust efficient of upstream wind turbines in the wind farm influences the downstream wind speed which determines the available wind power of the downstream wind turbine. Optimize the wind power production of each wind turbine in the wind farm by the optimization of the pitch angle and tip-speed-ratio of each turbine can increase the total available wind power of the wind farm. This paper proposed a new dispatch method which dispatches the wind power reference to each wind turbine proportional to each wind turbine's optimal wind power to increase the available wind power of the whole wind farm. Particle Swarm Optimization (PSO) is used to obtain the optimal wind power for each wind turbine. A case study is carried out. The available wind power of the wind farm was compared between the traditional dispatch method and the proposed dispatch method with the consideration of the wake effect.

<strong class="journal-contentHeaderColor">Abstract.</strong> The development of wind energy in recent years has made the representation of wind turbines in numerical models essential for wind resource and environmental assessment. In this study, we introduce a new wind turbine model, the Simple Actuator Disk for Large Eddy Simulation (SADLES), parameterized in the Weather Research and Forecast (WRF) model. Our goal is to perform realistic downscaling of large eddy simulation, so we integrated SADLES and the cell perturbation method as WRF modules and released the code as open-source for further research. We demonstrate the effectiveness of SADLES by comparing a large eddy simulation of a 5-MW wind turbine using the WRF-SADLES with the Parallelized Large-eddy simulation Model (PALM), showing that SADLES can simulate wind turbine wakes effectively at an intermediate resolution of a few dozen meters. Furthermore, we show the applicability of SADLES in wind farm assessment by downscaling the ERA5 reanalysis data to study turbine-to-turbine and farm-to-farm interactions for the Alpha Ventus wind farm. In our case study, due to the wind farm to the southwest, the ambient wind speed at Alpha Ventus is reduced by 14 % and the average turbine power is reduced by 35 %. SADLES provides a promising balance between computational efficiency and accuracy for wind turbine wake simulations and has potential applications in wind energy assessment and wind farm construction planning.

This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.

Power loss mechanisms in large wind farms are complex due to the multiscale nature of wind farm aerodynamics. Recent studies based on ‘two-scale momentum theory’ have brought new insights into this field; however, most of them have been limited to idealised wind farm scenarios. To better understand power loss mechanisms in real wind farms, in this study, we extend the framework of the two-scale momentum theory to non-ideal turbine design and layout scenarios, and then introduce simple analytical models to account for the associated power losses. This extension provides a holistic view of how turbine design, layout, operating conditions and atmospheric conditions collectively determine the amounts of different types of power losses in real wind farms, including the losses due to turbine-wake interference (i.e. ‘internal’ power loss) and farm-atmosphere interaction (i.e. ‘external’ power loss). We also present a simple iterative method for calculating the optimal farm induction factor that maximises the overall farm power for a given set of conditions, including the atmospheric boundary layer height. Analogously to blade-element momentum theory playing a key role in wind turbine design optimisation, the present theory may play a key role in wind farm design optimisation.

Abstract. Models used in wind resource assessment (WRA) range from engineering wake models and computational fluid dynamics models to mesoscale weather models with wind farm parameterizations and, more recently, large-eddy simulation (LES). The latter two produce time series of wind farm power of a certain period. This simulation period is, in the case of LES, mostly limited to ≤ 1 year due to the computational costs. However, estimates of long-term (O(10 years)) power production are of high value to many parties involved in WRA. To address the need to calculate long-term annual energy production from ≤ 1-year model runs, therefore, this paper presents methods to estimate the long-term (O(10 years)) power production of a wind farm using a ≤ 1-year simulation. To validate the methods, a 10-year LES of a hypothetical large offshore wind farm is performed. The methods work by estimating the conditional probability densities between wind farm power from the LES and wind speed from reanalysis data (ERA5) from a short (≤ 1 year) LES run. The conditional probability densities are then integrated over 10 years of ERA5 wind speed, yielding an estimate of the long-term mean power production. This “long-term correction” method is validated on varying simulation periods, selected with four different day-selection techniques. When applied to a simulation period of 365 consecutive days, the methods can estimate the 10-year mean power production with a mean absolute error of around 0.35 % of the long-term mean. When choosing the simulation period with day-selection techniques that represent the long-term climate, only roughly 200 simulation days are needed to achieve the same accuracy. Finally, a method to also include wind observations in the long-term correction is presented and tested. This requires an additional “free stream” LES run without active turbines and gives estimates of long-term power and wind that are corrected for a potential LES bias. Although validation of this final approach is difficult in the employed modeling strategy, it gives valuable insights and fits within the common WRA practice of combining models and observations. The presented techniques are based on physical arguments, computationally cheap, and simple to implement. Furthermore, they are not limited to LES but can be applied to other time-series-based models. As such, they could be a useful extension for the diverse set of modeling, observational, and statistical techniques used in WRA.