Wind Farm Research Articles

Numerical Investigation of Wake Characteristics for Scaled 20 kW Wind Turbine Models with Various Size Factors

Wind energy is essential for sustainable energy development, providing a clean and reliable energy source through the wind turbine. However, the vortices and turbulence generated as wind passes through turbines reduce wind speed and increase turbulence, leading to significant power losses for downstream turbines in wind farms. This study investigates wake characteristics in wind turbines by examining the effects of different scale ratios on wake dynamics, using both experimental and numerical approaches, utilizing scaled-down models of the Aeolos H-20 kW turbine at scales of 1:33, 1:50, and 1:67. The experimental component involved wind tunnel tests in an open-circuit tunnel with adjustable wind speeds and controlled turbulence intensity. Additionally, Computational Fluid Dynamics (CFD) simulations were conducted using STAR-CCM+ (Version 15.06.02) to numerically analyze the wake characteristics. Prior to the simulation, a convergence test was performed by varying grid density and y+ values to establish optimized simulation settings essential for accurately capturing wake dynamics. The results were validated against experimental data, reinforcing the reliability of the simulations. Despite minor inconsistencies in areas affected by tower and nacelle interference, the overall results strongly support the methodology’s effectiveness. The discrepancies between the experimental results and CFD simulations underscore the limitations of the rigid body assumption, which does not fully account for the deformation observed in the experiment.

Improving short-term wind power forecasting in Senegal’s flagship wind farm: a deep learning approach with attention mechanism

Improving short-term wind power forecasting in Senegal’s flagship wind farm: a deep learning approach with attention mechanism

A Nonlinear Wind Turbine Wake Expansion Model Considering Atmospheric Stability and Ground Effects

This study investigates the influence of atmospheric stability and ground effects on wind turbine wake recovery, challenging the conventional linear relationship between turbulence intensity and wake expansion coefficient. Through comprehensive field measurements and numerical simulations, we demonstrate that the linear wake expansion assumption is invalid at far-wake locations under high turbulence conditions, primarily due to ground effects. We propose a novel nonlinear wake expansion model that incorporates both atmospheric stability and ground effects by introducing a logarithmic relationship between the wake expansion coefficient and turbulence intensity. Validation results reveal the superior prediction accuracy of the proposed model compared to typical engineering wake models, with root mean square errors of wake wind speed predictions ranging from 0.04 to 0.063. The proposed model offers significant potential for optimizing wind farm layouts and enhancing overall wind energy production efficiency.

GIS-Based Optimal Siting of Offshore Wind Farms to Support Zero-Emission Ferry Routes

To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities.

Comprehensive assessment of the climatic and vegetation impacts of wind farms on grasslands: A case study in inner Mongolia, China

Although wind power contributes to the reduction of greenhouse gas emissions, it also has significant impacts on the local climate and vegetation. Exploring these impacts is important for the sustainable development of wind power. Therefore, based on moderate-resolution imaging spectroradiometer (MODIS) data and other remote sensing data from 2003 to 2022, this paper investigated the impacts of 101 grassland wind farms (WFs) in Inner Mongolia on land-atmosphere water and heat exchange, vegetation growth, ecosystem primary productivity, and vegetation structural characteristics during the growing season and revealed the spatial distribution patterns of the impacts of WFs as well as differences between different types of grasslands. The results indicated that WFs increased the nighttime land surface temperature (LST), decreased evapotranspiration (ET), inhibited vegetation growth, decreased gross primary productivity (GPP), and reduced the leaf area index (LAI) in growing season grasslands. This effect varied across different types of grasslands and showed significant complexity. In terms of land-atmosphere water and heat exchange, nighttime LST increases and ET decreases were significant in the typical steppe but not in the meadow steppe. In terms of vegetation change, meadow steppe had the most inhibited vegetation growth and the greatest reduction in GPP. In terms of the impact range, WFs on typical steppe and meadow steppe have opposite effects on vegetation growth and ecosystem primary productivity inside and outside of them, i.e., they inhibit vegetation growth and reduce GPP inside the WF areas but promote vegetation growth and increase GPP outside the WF areas. Compared with previous studies, this study analyzed multiple climate and vegetation indicators based on many WF samples, which reduced the uncertainty associated with a single sample and provided more comprehensive and comparable observations of different types of grasslands. These findings can help to balance the relationship between wind power development and ecological protection.

Optimizing Price Markup: The Impact of Power Purchase Agreements and Energy Production Uncertainty on the Economic Performance of Onshore and Offshore Wind Farms

Wind energy is rapidly expanding its capacity as part of the global energy transition. To ensure economic viability, wind energy projects increasingly rely on risk mitigation strategies. While Power Purchase Agreements (PPAs) manage spot market price variability, wind farms still face energy production uncertainty, directly impacting the quantity of energy generated. This study presents a comprehensive analysis of the price markup for onshore and offshore wind farms, considering energy production uncertainty under PPA and non-PPA scenarios. The study examines key metrics such as equilibrium prices, price markups, Internal Rate of Return (IRR), Net Present Value (NPV), and their correlation to energy production risk. Results demonstrate that PPAs significantly alter the relationship between price markup and variability. Offshore wind farms can potentially benefit more from PPAs compared to onshore, especially at lower levels of wind energy variability. However, as variability increases, the risk mitigation provided by PPAs diminishes, and both onshore and offshore wind farms may require higher price markups for financial viability. These findings highlight the necessity of carefully designed PPA structures and pricing strategies to ensure long-term competitiveness and sustainability of wind energy projects.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China

The rapid growth of offshore wind farms (OWFs) is driven by concerns for energy security and climate change mitigation. However, their impact on marine environments remains poorly understood due to limited research. This study analyzes the effects of an OWF along China's Jiangsu Coast on seawater quality using data from different development phases. Results show the major pollutants were different across phases. Heavy metal pollution reached alert levels during construction compared to the safe levels observed in the pre-construction and operational phases, mainly due to increases in Pb, Cd, and Hg concentrations. Eutrophication was mild throughout all periods but exhibited a continuous decrease, primarily attributed to reductions in PH and COD concentrations. As a result, the comprehensive pollution level during construction was increased, but it was improved to a clean level during the operational phase. Besides, significant variations were observed in the spatial distribution patterns of major pollutant indices across different scenarios. These changes may stem from a combination effect of land-based pollution, aquaculture, OWF-induced disturbances to atmosphere and hydrodynamics, OWF-related drain and leakage contamination, and marine management policies. Understanding these effects informs OWF optimization, rational wind resource utilization, and marine ecology protection.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm

Wind farm layout optimization (WFLO) has become a significant approach to enhancing the efficiency of wind energy utilization. However, load also represents a critical factor that must be considered during optimization. To enhance power generation while controlling loads within limitations, an innovative load-constrained layout optimization method that employs a surrogate model based on artificial neural networks and genetic algorithms was proposed. This paper verified the accuracy of the surrogate model and then conducted layout optimizations on a single and a full wind condition case to assess the proposed method. The results indicated that the mean absolute percentage errors of load channels can meet the precision requirements for WFLO. A comparison of layout optimization results between the method proposed in this paper and the traditional method showed that in the single-wind-condition case, the method proposed in this paper reduced the maximum load by 5.64 % compared to the traditional method, with nearly identical power output; in the full-wind-condition case, the reduction of maximum load was 1.70 %, while only sacrificing the annual power generation by 0.10 %. This study provides a load-constrained WFLO method, promising to effectively ensure the lifespan of wind turbines while increasing power generation and offering significant engineering value.

A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Development and validation of a three-dimensional wind-turbine wake model based on high-order Gaussian function

Under natural conditions, the wake velocity distribution approximates top-hat shape in the near wake center, and a Gaussian shape in the far wake. To improve the prediction accuracy of the wake distribution, this paper proposes a three-dimensional high-order Gaussian linear entrainment wake model by modifying the wake velocity distribution with high-order Gaussian function. Field tests and wind tunnel tests are then conducted. The obtained results show that the proposed model can accurately describe the radial and vertical spatial distributions of the wake flow. Under different wind speeds, the maximum and minimum radial relative errors are respectively 7.29% and 1.4%, while the maximum and minimum vertical relative errors are respectively 7.64% and 3.87%. This demonstrates that the proposed model can accurately predict the velocity distribution in the whole wake region. The proposed model is simple, has low cost, and exhibits high accuracy. Thus, it can be used in wind farm layout optimization and wake control.

Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.

Research on Aerodynamic Performance of Asynchronous-Hybrid Dual-Rotor Vertical-Axis Wind Turbines

This study analyzes the performance degradation of traditional hybrid wind turbines under high blade-tip-speed ratio conditions and proposes solutions through two-dimensional Computational Fluid Dynamics (CFD) simulations. It also introduces the design of two innovative asynchronous-hybrid dual-rotor wind turbines. The results indicate a remarkable 98.5% enhancement in torque performance at low blade-tip-speed ratios with the hybrid wind turbine model. However, as the blade-tip-speed ratio increases, it leads to negative torque generation within the inner rotor of the conventional design, resulting in a reduction of the power coefficient by up to 13.1%. The introduction of the new wind turbine design addresses this challenge by eliminating negative torque at high blade-tip-speed ratios through adjustments in the inner rotor’s operating range. This modification not only rectifies the negative torque issue but also enhances the performance of the outer rotor in the leeward region, consequently boosting the overall power coefficient. Moreover, the optimized inner rotor configuration effectively disrupts and shortens the wake length by 16.7%, with this effect intensifying as the rotational speed increases. This optimization is pivotal for enhancing the efficiency of multi-machine operations within wind farms.

Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices.

Optimization of wind farm layout to maximize the energy yield

Over time, the use of wind turbines has evolved in terms of the size and number of the turbines. The change in wind turbines comes as a result of major structural changes and then an increase in their production capacity. In the framework of the most stable operation for the surface of the terrain used in this study, a series of configurations have been proposed that are directly related to technical and economic sustainability. The complete scenarios will be realized in Koznica, where one-year measurements are done. In the framework of the realized scenarios, it can be seen that there is variability in terms of energy output from these configurations, which maximally comes from wake losses caused by the terrain. In cases where the scenarios with the addition of small turbines are studied, they, in the technical aspect, affect the increase in energy production, which, if those turbines were not installed, would not be applied. As an important part, the economic aspect of the realization of the previous optimization scenarios is analyzed by using Monte-Carlo simulation for LCOE analysis. Thus, it can be concluded that the analyzed scenarios with the addition of small turbines in the optimization process are more suitable and feasible for implementation.

Eplingiella sanoi sp. nov. (Hyptidinae-Lamiaceae): supports the urgent need for campos rupestres conservation in the Serra do Espinhaço Septentrional, Minas Gerais state, Brazil

A new species of Lamiaceae, Eplingiella sanoi, from the campo rupestre of an ecotone zone between the Cerrado and the Caatinga domains is described and illustrated. The new species expands the distribution of the genus to Southeast Brazil, previously restricted to Northeast Brazil. Eplingiella sanoi is compared to the three other species of the genus, especially E. cuniloides, the morphologically closest related species. We present an identification key for all the species of the genus, an occurrence map for Eplingiella sanoi, E. cuniloides and E. brightoniae and a formal conservation assessment for the new species. Eplingiella sanoi is endemic to the region between Pico da Formosa and Pico do Sucuruiú (Minas Gerais state, Serra do Espinhaço), an area under threat due to a planned wind farm installation. Recognizing this species emphasizes the imperative for more taxonomic studies and conservation of Serra do Espinhaço.

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

The applicability study of the large wind farm wake effect model

Abstract As the scale of the wind farm becomes bigger and the wind turbines are increasingly clustered, a large wind farm wake effect occurs. In this work, the initial distance where the large wind farm wake effect model starts to take effect is investigated analytically. The large wind farm wake effect is evaluated by constructing a large-scale wind farm with a regular arrangement of wind turbines similar to Horns Rev. The essential variables that influence the wind deficit trend of the large wind farm wake effect model are explored numerically. The wake deficit becomes more obvious as the surface roughness gets lower, the apparent turbine spacing gets smaller, or the thrust coefficient decreases. The wake deficits between the large wind farm wake effect model and the wake superposition of the modified Park model are compared. The transition distance, where the dominator of the wind deficit switches from the modified Park model to the large wind farm wake effect model, moves upstream along the incoming wind for the rougher surface or the lower thrust coefficient. In addition, for the similar resultant turbine spacing, the slightly staggered wind farm layout does not show an obvious influence on the wake deficit obtained by the large wind farm wake effect model.

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.