Comment on egusphere-2023-491

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.

The development and utilization of wind power to meet the electrical demand has recently received significant consideration. Additionally, one of the substantial roles of transmission system operators is to balance reactive power within a network in the sense that with the development of wind energy, wind turbines are expected to contribute towards reactive power generation. So far, there is not a wide range of attention being paid to reactive power in reliability evaluation including wind farms contribution. In this article, wind farms with several identical wind turbines are incorporated in reliability assessment while considering reactive power shortage. Fuzzy C-Means clustering method is used for the output power of wind turbines for analyzing power system reliability with wind farms integration. The application of this concept has been also utilized in developing multistep load levels in the illustrated load buses. Reactive power deficiency and the relevant voltage violations caused by the failure of reactive power generations are studied in this article. Load shedding and power injection techniques are employed to determine possible reactive power shortage required for alleviating network violations with and without wind power integration. Composite system reliability analysis in the existence of wind farms is implemented to assess indices affiliated with curtailed energy at various load points. The RBTS 6-bus system has been proportionally modified and studied to demonstrate the procedure. The results indicate the importance of wind power integration in improving both active and reactive reliability indices which in turn provide system planners with long-range planning for system development.

A challenge to simulating turbulent flow in multiscale atmospheric applications is the efficient generation of resolved turbulence motions over an area of interest. One approach is to apply small perturbations to flow variables near the inflow planes of turbulence-resolving simulation domains nested within larger mesoscale domains. While this approach has been examined in numerous idealized and simple terrain cases, its efficacy in complex terrain environments has not yet been fully explored. Here, we examine the benefits of the stochastic cell perturbation method (CPM) over real complex terrain using data from the 2017 Perdigão field campaign, conducted in an approximately 2-km wide valley situated between two nearly parallel ridges. Following a typical configuration for multiscale simulation using nested domains within the Weather Research and Forecasting (WRF) model to downscale from the mesoscale to a large-eddy simulation (LES), we apply the CPM on a domain with horizontal grid spacing of 150 m. At this resolution, spurious coherent structures are often observed under unstable atmospheric conditions with moderate mean wind speeds. Results from such an intermediate resolution grid are often nested down for finer, more detailed LES, where these spurious structures adversely affect the development of turbulence on the subsequent finer grid nest. We therefore examine the impacts of the CPM on the representation of turbulence within the nested LES domain under moderate mean flow conditions in three different stability regimes: weakly convective, strongly convective, and weakly stable. In addition, two different resolutions of the underlying terrain are used to explore the role of the complex topography itself in generating turbulent structures. We demonstrate that the CPM improves the representation of turbulence within the LES domain, relative to the use of high-resolution complex terrain alone. During the convective conditions, the CPM improves the rate at which smaller-scales of turbulence form, while also accelerating the attenuation of the spurious numerically generated roll structures near the inflow boundary. During stable conditions, the coarse mesh spacing of the intermediate LES domain used herein was insufficient to maintain resolved turbulence using CPM as the flow develops downstream, highlighting the need for yet higher resolution under even weakly stable conditions, and the importance of accurate representation of flow on intermediate LES grids.

Abstract. Terrain-induced flow phenomena modulate wind turbine performance and wake behavior in ways that are not adequately accounted for in typical wind turbine wake and wind plant design models. In this work, we simulate flow over two parallel ridges with a wind turbine on one of the ridges, focusing on conditions observed during the Perdigão field campaign in 2017. Two case studies are selected to be representative of typical flow conditions at the site, including the effects of atmospheric stability: a stable case where a mountain wave occurs (as in ∼ 50 % of the nights observed) and a convective case where a recirculation zone forms in the lee of the ridge with the turbine (as occurred over 50 % of the time with upstream winds normal to the ridgeline). We use the Weather Research and Forecasting Model (WRF), dynamically downscaled from the mesoscale (6.75 km resolution) to microscale large-eddy simulation (LES) at 10 m resolution, where a generalized actuator disk (GAD) wind turbine parameterization is used to simulate turbine wakes. We compare the WRF–LES–GAD model results to data from meteorological towers, lidars, and a tethered lifting system, showing good qualitative and quantitative agreement for both case studies. Significantly, the wind turbine wake shows different amounts of vertical deflection from the terrain and persistence downstream in the two stability regimes. In the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub height at four rotor diameters downstream. In the convective case, the wake deflects above the recirculation zone over 40 m above hub height at the same downstream distance. Overall, the WRF–LES–GAD model is able to capture the observed behavior of the wind turbine wakes, demonstrating the model's ability to represent wakes over complex terrain for two distinct and representative atmospheric stability classes, and, potentially, to improve wind turbine siting and operation in hilly landscapes.

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

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.

Wind farms with counter-rotating wind turbines

Large-eddy simulation of a utility-scale wind farm in complex terrain

The effect of the boundary layer on the wake of a horizontal axis wind turbine

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.

<p>As is known, wind is a renewable and non-polluting energy resource. In addition, there is no transportation problem in wind energy and it does not require very high technology for electricity generation. Wind turbines are used for electricity generation from kinetic energy of wind. In the point of power curves of these turbines, wind speed must be a certain band. Generally, they do not generate electricity cut-in wind speed that is between 0 and 4 m/s and cut out wind speed that is over 20-25 m/s. Over cut-out values cause breaking down of wind turbines, because high wind speeds create extra mechanical loads on them. Therefore, maximum/extreme winds and their estimation and prediction carry weight in terms of energy generation.</p><p>New European Wind Atlas (NEWA) is the project, within the scope of ERANET+ Program, and the attendants are Belgium, Denmark, Germany, Latvia, Portugal, Spain, Sweden, and Turkey. The aim of NEWA is to present a new wind atlas to the wind industry. In this project, the physical model used for obtaining wind speeds is a numerical weather prediction model named Weather Research and Forecasting (WRF).</p><p>One of the methods, which are developed by imitating of biological properties of living forms in a virtual environment, is Artificial Neural Networks (ANNs). Stimulations taken from the environment by using sense organs are transmitted to brain whereby neurons in a body and brain makes a decision towards these stimulations. That is the working form of ANNs. Moreover, ANNs can be thought as a black box, which processes given data and produces outputs against inputs. Furthermore, they are a method of Artificial Intelligence.</p><p>In this study, maximum wind speeds of 4 different wind farms in Turkey were estimated by using a downscaling method based on ANNs and wind data which were produced in grid points of NEWA Project. Besides that, 8 different levels (10, 50, 75, 100, 150, 200, 250, and 500 m) for each wind farm were considered. As a result of determining the best ANN architectures with sensitivity analysis, it was seen that Levenberg-Marquardt Backpropagation (trainlm) approach as a training algorithm and 9 neurons in each layer are common traits of best ANN architectures. In addition, 50 m for 2 wind farms and 10 m with 75 m for others were determined as an optimum downscaling levels. Moreover, according to downscaling results, correlation values were calculated around 0.80.</p><p><strong>Key Words: </strong>ANN, Downscaling, Maximum wind, NEWA, Turkey, Wind farm.</p>

The development of onshore wind energy is linked to the conditions for landscape protection and development established during the implementation of the European Landscape Convention (ELC). In Poland, the implementation of the ELC results in the designation and protection of priority landscapes, which may restrict the construction of new wind farms. The widespread ratification of the ELC by European countries where wind farms are being developed makes the possibility of limiting wind energy development through ELC implementation an important issue from a research and practical perspective. Experience from Poland can be helpful in optimizing the implementation process without impacting the total installed capacity of wind farms. Using GIS tools and a multi-criteria assessment of the conditions for excluding areas from wind energy development in Poland, the scale of territorial barriers was assessed in the variants without and with priority landscapes. The resources available for wind farms and the reduction in these resources associated with the implementation of the ELC were assessed quantitatively and spatially. The amount of capacity that can be connected and that will be limited by the implementation of the ELC was estimated. The analysis was conducted for the country, its regions, and zones with varying wind conditions predisposing to wind energy development. An approximately 5% reduction in the territorial potential for onshore wind energy development was observed due to the implementation of the ELC. Significant spatial variation was observed, including regional limitations on wind farms associated with the implementation of the ELC. Spatial barriers to development result not only from the presence of high-quality landscapes but also from varying regional policies for their protection and shaping. There is a lack of national coordination of regional policies for the implementation of the ELC, and there is no coordination of this process with other plans and strategies, including energy transformation and security. Despite the identified limitations to wind energy development, no threat to the achievement of strategic wind energy development goals related to connecting new capacity by 2030 and 2040 has been identified. However, in the longer term, as areas available for wind energy development become increasingly scarce, implementing ELC may pose a significant barrier to energy transition. Research indicates that the ELC implementation model in Poland, which emphasizes landscape protection rather than landscape planning and sustainable management, is not beneficial for onshore wind energy. It is necessary to integrate landscape protection policy with energy transition policy, particularly in zones with the most favorable wind (economic) conditions for onshore wind energy development.

Wind energy is one of the fastest-growing renewable energy technologies. One of the environmental concerns associated with wind energy development is noise emission from wind energy facilities, especially the low-frequency noise and infrasound that allegedly raises public health concerns. In order to analyze the noise emission from wind turbines and to aid future wind energy development, a numerical simulation tool for the prediction of low-frequency noise and infrasound emission from wind turbines is developed. The simulation tool is a low-order model developed by combining aerodynamic noise theories and basic working principles of wind turbines. 2-D airfoil aerodynamics calculation is coupled with a subroutine that solves Ffowcs Williams-Hawkings (FW-H) equation in time domain. By omitting unsteady aeroacoustics mechanisms, the low-order simulation model is capable of computing infrasound emissions from multiple wind turbines simultaneously. By utilizing the low-order simulation model, the effects of wind directions, wind turbine locations, and phases of wind turbine rotations on the consequent aerodynamic infrasound are studied. Results of aerodynamic infrasound computation imply that wind farm configuration or wind turbine phase adjustment can help reduce the noise level at certain locations, which makes the low-order simulation model a useful tool to aid wind farm siting and controlling.

Accessibility for operation and maintenance tasks in co-located wind and wave energy farms with non-uniformly distributed arrays

The multi-rotor (MR) wind turbine concept can be used to upscale wind turbines without increasing the rotor diameter, which can be beneficial for transport, manufacture and design of wind turbines blades. The rotor interaction of a MR wind turbine leads to a faster wake recovery compared to an equivalent single-rotor (SR) wind turbine wake. In this article, the benefit of the faster wake recovery of MR wind turbines is quantified using Reynolds-averaged Navier-Stokes simulations of a 4}4 rectangular MR wind farm, for three different inter wind turbine spacings. The simulations predict an increase of 0.3-1.7% in annual energy production for the MR wind farm with respect to an equivalent SR wind farm, where the highest gain is obtained for the tightest inter wind turbine spacing. The gain in AEP is mainly caused by the aligned wind directions for the first downstream wind turbine in a wind turbine row of the MR wind farm, which is verified by an additional large-eddy simulation.