Improve Wind Turbine Research Articles

Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement

The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer. HARO is the proposed model to traditional models, including the Huber and Automatic Relevance Determination (ARD) Regressors, since Transformers are capable of learning the patterns of the sensors. The overall analysis of the results obtained illustrated that the Housing Asset Repair Optimization tool had reduced downtime while enhancing, at the same time, the accuracy of the prediction of future faults to enable the right-time planning for maintenance activities. This aspect enhances the learning process, minimizing human input and hence slightly lessens the efficiency of the turbines in HARO. The study further calls for collective research practice amongst researchers, practitioners and policymakers to address emerging issues in wind power maintenance to avoid duplication and continuously keep improving on the strategies employed towards the maintenance of wind energy. This paper also shows that ML has the possibility of improving wind turbine dependability and establishing wind energy as an essential component of renewable energy systems.

Finite element submodeling technique-based fatigue analysis and reliability modeling of wind turbine blade trailing edge

Wind turbine blades play a critical role in wind turbine systems, with the trailing edge bearing significant mechanical loads. During operational cycles, the adhesively bonded composite trailing edge may fracture, delaminate, or buckle, posing a safety risk for wind turbine systems. While finite element simulation is commonly used to evaluate blade fatigue performance due to the challenges associated with full-scale structural tests in terms of costs and time, current methodologies mainly focus on the overall fatigue characteristics of blades, neglecting specialized analyses. A finite element submodeling approach is presented here to address this research gap by analyzing wind turbine blade trailing edges for fatigue and reliability. Specifically, a finite element submodelingmethodis proposedtoanalyzelocal fatiguefailuresof wind turbine blades. This approach is validated through fatigue testing on conventional composite bonded specimens. Subsequently, failure analyses and life predictions are conducted on the trailing edges to investigate their fatigue behavior, followed by an exploration of the impact of submodeling techniques on the analysis results. Furthermore, considering material and dimensional uncertainties, a fatigue reliability model for trailing edges is developed. The results demonstrate that this approach effectively overcomes the limitations of overall blade finite element analysis by enabling localized fatigue analysis of the trailing edge, providing valuable insights for improving wind turbine blade design optimization.

CFD analysis of key factors impacting the aerodynamic performance of the S830 wind turbine airfoil

The Global Wind Energy Council (GWEC) reported that in 2021, global wind capacity increased by 93.6 GW (+12%), reaching a total of 837 GW, most of which was contributed by wind turbines. Improving wind turbine performance primarily hinges on advancements in blade technology and airfoil design. This study examines the effect of profile thickness on the aerodynamic performance of the S830 airfoil at a Reynolds number of 25,148, as part of the NREL airfoil's aerodynamic performance research. However, the impacts of additional variables—such as angle of attack, Reynolds number, speed range, and particularly ice accretion—have not been thoroughly investigated. This study utilized a 2D CFD model provided by the commercial software ANSYS Fluent and FENSAP-ICE. The lift-to-drag ratio of the S830 wind turbine airfoil was examined, considering the effects of angle of attack, wind speed, Reynolds number, airfoil thickness, and ice accretion. Additionally, design-related solutions will be suggested. The research indicates that the optimal angle of attack increases the lift-to-drag ratio by approximately 250% compared to a zero angle of attack. An increase in wind speed causes this coefficient to rise nonlinearly within the studied velocity range. The Reynolds number directly influences the optimal angle of attack. According to CFD results, the lift-to-drag ratio can be increased by 50% if the airfoil's thickness is reduced by 20% compared to the original profile. For the ice accretion simulation model, a case test of the NACA 0012 airfoil was conducted to verify the model's parameters. Subsequently, a survey of this phenomenon on the S830 airfoil revealed that the lift coefficient decreased by 5.25% after 90 minutes of ice accretion.

Predicting the impact of wind turbine noise: general overview and results of the PIBE project

The PIBE project aims to improve wind turbine noise prediction methods and to explore new solutions for noise reduction. This five years project brought together French experts in aeroacoustics, sound propagation, experimental noise characterization and wind engineering, and was structured around three work packages. The first one aimed to study amplitude modulation phenomena and focused particularly the characterization of dynamic stall noise. Specific aerodynamic and acoustic measurements were carried out in a wind tunnel, showing the influence of several stall regimes on noise production. The second work package focused on quantifying the uncertainties of noise prediction methods. It developed an open-access online application (WindTUNE) that quantifies uncertainties on noise prediction of a wind farm, and a parametric and uncertainties calculation tool for the engineering application Code-TYMPAN. This axe also produced an open-access database of a 400 days campaign of meteorological and acoustical measurements around a wind farm. The last work package investigated new noise reduction devices using blades with modified leading and/or trailing edges. The efficiency of the solutions were characterized in a wind tunnel, both acoustically and aerodynamically. The paper presents the main results obtained at the end of the project.

Computational Fluid Dynamics (CFD) Investigation of NREL Phase VI Wind Turbine Performance Using Various Turbulence Models

This study presents a detailed computational fluid dynamics (CFD) investigation into the aerodynamic performance of the NREL Phase VI wind turbine, focusing on torque and power generation under different turbulence models. The primary objective was to analyse the effect of various turbulence models and their responses in wind turbine torque generation. Furthermore, it also investigates the distance effect on wind velocity deficit. The research utilizes 2D and 3D simulations of the S809 airfoil and the full rotor, examining the predictive capabilities of the k-epsilon, k-omega, and k-omega SST turbulence models. The study incorporates both experimental validation and wake analysis using the Gaussian wake model to assess wind velocity deficits. Simulations were conducted for a wind speed range of (6–10 m/s), with results indicating that the k-epsilon model provided the closest match to experimental data, particularly at higher wind speeds within the targeted range. Even though k-epsilon results had better agreement when validated with experimental data, theoretically k-omega (SST) should perform better as it combines k-epsilon and k-omega advantages in predicting the flow regardless of its farness from the wall. However, in simulations using the k-omega (SST), the separation of flow and the shear stress transients were only visible at wind speeds of 10 m/s or higher. Wake effects, on the other hand, were found to cause significant velocity deficits behind the turbine, following an exponential decay pattern. The findings offer valuable insights into improving wind turbine performance through turbulence model selection and wake impact analysis, providing practical guidelines for future wind energy optimizations.

Computational Fluid Dynamics in Wind Energy: Modeling and Optimization for Turbine Design

Renewable energy sources are hard to come by, but wind power has become one of the most popular options because it can make a lot of clean electricity. For wind energy to be used properly, however, turbines need to be designed in a way that takes into account the surrounding environment. To model and improve wind turbine systems, computational fluid dynamics (CFD) has become very important. It lets engineers simulate complicated fluid flow processes and improve turbine performance. When it comes to wind energy, this study mainly talks about how CFD can be used to model and improve rotor designs. Experts can learn more about the complicated fluid dynamics around wind turbines by using CFD models. For example, they can learn how the rotor blades interact with the wind flow moving in. Calculative fluid dynamics (CFD) makes it easier to guess things like power output, efficiency, and structure loads for wind turbines by accurately modeling turbulence, boundary layers, and other airflow effects. An important other topic this paper covers is how to make wind machine designs work better. To find the best setups for capturing energy while using the least amount of materials and spending the least amount of money on upkeep, engineers can combine computational fluid dynamics (CFD) with optimization methods. Blade shape, tower height, turning angle, and rotor speed are just some of the design factors that this optimization process looks at. We can make very efficient and cost-effective wind turbine systems that are perfect for each spot by using CFD-driven optimization methods and running models and analyses over and over again. For better confidence and accuracy in turbine design predictions, this study talks about how to combine CFD models with actual confirmation. Engineers can make sure that CFD models work in a wide range of situations by comparing modeling results with readings taken in the real world. It also helps to improve the models. This paper emphasizes how important CFD is to the progress of wind energy technology by giving a thorough look at its uses in designing turbines and their optimization, proof, and development. Potential for improving the efficiency and long-term viability of wind power generation stays high as long as more study and new ideas are put into CFD-driven methods.

Indonesia's Breakthrough in Maximizing Wind Energy Efficiency Through Optimized Blade Configuration

Indonesia faces rising energy demand, risking an energy crisis and increased fossil fuel dependence. This study explores using NACA 0021 airfoil blades to improve wind turbine performance by analyzing the effect of different rotor blade numbers. Conducted at Universitas Muhammadiyah Sidoarjo, the research tested turbines with 2, 3, and 4 blades, measuring current, voltage, power, and efficiency. Results showed that more rotor blades enhance turbine performance, increasing current and voltage output. This suggests optimizing blade configurations can significantly boost small-scale wind energy systems, helping reduce fossil fuel reliance and mitigate the energy crisis. Highlights: 1. More rotor blades boost turbine performance: increased current and voltage.2. NACA 0021 airfoil optimizes small-scale power generation.3. Effective blade configuration reduces fossil fuel reliance, mitigating energy crises. Keywords: wind energy, NACA 0021, rotor blades, turbine performance, renewable energy

The Effects of a Seagull Airfoil on the Aerodynamic Performance of a Small Wind Turbine

Birds’ flight characteristics such as gliding and dynamic soaring have inspired various optimizations and designs of wind turbines. The implementation of biological wing geometries such as the airfoil profile of seabirds has improved wind turbine performance. However, the field can still benefit from further investigation into the aerodynamic characteristics of an inspired design. Therefore, this study evaluated the effect of a seagull airfoil design on the aerodynamic performance of the National Renewable Energy Laboratory (NREL) Phase VI wind turbine. By replacing its S809 airfoil with the laser-scanned profile of the seagull airfoil, the aerodynamic behavior at key locations of the NREL Phase VI wind turbine blade was numerically simulated in a three-dimensional environment using the Ansys Fluent 2022 R1 computational fluid dynamics (CFD) code. The results were validated against the experimental data, and analysis of the torque outputs, pressure distributions, and velocity profiles that were generated by both the baseline and modified models demonstrated the ability of the seagull airfoil profile to modify regions of minimum and maximum local velocities to achieve highly favorable pressure differentials, significantly increasing the torque output of the NREL Phase VI wind turbine by 350, 539, 823, and 577 Nm at 10, 15, 20, and 25 m/s inlet velocities, respectively.

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.

CFD investigation on wind power harvesting from small-scale wind turbines for powering residential buildings in New Zealand

ABSTRACT As an alternative solution, wind power could be harnessed by designing and implementing small-scale wind turbines for distributed power generation. In this work, 3-dimensional time-domain numerical simulations, based on the Reynolds-Average Navier-Strokes (RANS) model, are conducted for two categories of small-scale wind turbines, i.e. drag force- (Savonius) and lift force-driven (Darrieus) ones, while placing them at a flat surface and a step height, respectively. The present results confirmed that placing a small-scale wind turbine at a step height can improve the maximum power output, respectively, approximately 219.2% for the Savonius wind turbine and 121.0% for the Darrieus wind turbine operating at their optimum stage; moreover, the corresponding peak power spectral density of the turbine torque is improved by 173.9% and 83.6%, respectively. In addition, the power generated from the wind flow could reduce the CO2 emission. It has been observed that the Savonius turbine could reduce the annual CO2 emission by 3,738.9–19,857.8 kg and that of Darrieus turbines by 4,919.6–26,128.8 kg. This study demonstrated the benefits of adopting forward facing steps to improve wind turbine performance in a typical urban environment.

A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks.

A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights

AbstractThe aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data‐driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data‐driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data.The authors’ review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models.

Numerical modelling and simulation analysis of wind blades: a critical review

Abstract Wind energy has emerged as a promising renewable energy source and wind turbine technology has developed rapidly in recent years. Improved wind turbine performance depends heavily on the design and optimization of wind blades. This work offers a critical evaluation of the state of the art in the field of numerical modelling and simulation analysis, which have become crucial for the design and optimization of wind blades. The evaluation of the literature includes considerable research on the application of numerical methods for the structural and aerodynamic performance of wind blades under various operating situations, as well as for analysis and optimization of wind blades. The article illustrates how numerical techniques can be used to analyse wind blade performance and maximize design efficiency. The study of blade performance under various wind conditions has also been made possible through the use of simulation analysis, thus enhancing the efficiency and dependability of wind turbines. Improvements in wind turbine efficiency and dependability, and ultimately the move towards a more sustainable energy future, will be greatly helpful for the development of numerical modelling and simulation techniques.

Control of seismic induced response of wind turbines using KDamper

Earthquake-induced vibrations of wind turbines may compromise structural serviceability and safety. Most previous studies adopted passive control devices to mitigate the seismic responses of wind turbines. However, their control effectiveness is heavily dependent on the mass ratio between control devices and wind turbines, and they were typically housed at the tower top or within the nacelle. The restricted space within the hollow tower and the nacelle imposes considerable challenges for the implementation of such devices, rendering the application of large-scale control devices unfeasible for structural vibration control of wind turbines. To this end, this paper integrates a negative stiffness element within a conventional tuned mass damper (TMD), termed KDamper, to mitigate vibrations of wind turbine towers under seismic loads. Specifically, the widely used NREL 5 MW wind turbine is selected as a prototype structure and its tower is modelled as a multiple-degree-of-freedom system. Then KDamper is incorporated into the developed model and its parameters are optimized based on the H2 criterion. Subsequently, the control effectiveness of KDamper is investigated and compared with TMD in the frequency domain, and the control performances in terms of the effectiveness and robustness of KDamper are further examined under a series of earthquake records. Results show that KDamper has superior control effectiveness and robustness than TMD, indicating it has considerable potential for application in improving wind turbine performances against earthquake hazards.

Application of an Open-Source OpenFoam for Fluid-Structure Interaction Analysis of the Horizontal-Axis Wind Turbine Blade

This study investigates numerical simulation for fluid-structure interaction in wind turbine blades, emphasizing the influence of dimensionless numbers. Utilizing OpenFoam, the Navier-Stokes equation is accurately solved with the PISO algorithm, ensuring proper interface conditions. The icoFsiFoam solver is validated through dynamic testing, demonstrating its effectiveness. In contrast to the widely adopted Blade Element Momentum Theory (BEMT), our approach focuses on analyzing blade deformation and resonance phenomena, capturing intricate deformations and stress concentrations. Our investigation explores the impact of reduced velocity on blade behavior across a range of 0.105 to 0.145, while consistently maintaining crucial dimensionless numbers such as Reynolds number (Re = 10⁶), Froude number (Fr = 4.93), and Cauchy number ( Cy = 10-5). The outcomes of this study significantly contribute to the understanding of fluid-structure interaction in wind turbine blades. By examining the oscillatory behavior of the blades, we observe trends similar to those predicted by BEMT. However, our approach surpasses BEMT by providing additional insights into stress concentrations and deformation modes. This advancement enables superior performance optimization and facilitates advanced blade analysis. The implications of our research are paramount for optimizing blade design and performance under varying reduced velocities. By incorporating the findings of this study, blade designers can make well-informed decisions to enhance the efficiency and durability of wind turbine technologies. The presented methodology and results provide a comprehensive investigation into the fluid-structure interaction of wind turbine blades, highlighting the importance of dimensionless numbers and their influence on blade behavior. Overall, this study offers valuable insights for improving wind turbine design and performance.

Performance Analysis of an Improved Gravity Anchor Bolt Expanded Foundation

With the continuous utilization of renewable energy, the number of onshore wind turbines is increasing. Small design improvements can save costs and facilitate the maintenance and repair of the wind turbine foundation. In this paper, an existing gravity expansion foundation with an anchor cage is improved. Our improvements further expand the space inside the foundation and reduce the length of the anchor bolt, which could reduce the costs and facilitate construction. To study the performance of the new foundation, a three-dimensional finite element model of the foundation–soil–anchor bolt was established via a finite element simulation. The damage evolution of the foundation was simulated with the concrete damage plasticity model (CDP). The separation ratio, foundation settlement, inclination ratio, reinforcement stress, foundation stress, and foundation damage of the new foundation under ultimate load conditions were analyzed. The influence of parameters h1 and b3 on the performance of the foundation was further studied. The finite element analysis results show that the tensile stress of concrete can be effectively reduced by appropriately increasing the corbel height and ring beam width of the foundation. The results also show that the improved wind turbine foundation force is reasonable and can meet the use of the actual project requirements on the level of finite element analysis.

Optimal maintenance strategy of wind turbine subassemblies to improve the overall availability

Improving wind turbine performance, operation, and efficiency requires evolving Operation and Maintenance (O&M) methods. Different models are required for O&M choices. These models offer a broad variety of scenarios and variables to reduce energy costs and enhance wind farm profitability. In this regard, this paper proposes a detailed approach for maintenance decisions to pick an optimum maintenance strategy (MST) for different subassemblies of wind turbines based on failure analysis. This approach is predicated mostly on a vast field database of failures and repairs of several subassemblies of wind turbines. The maintenance model is comprised of many stages, including the data preparation stage, the availability optimization stage, and the optimum MST selection step. Consequently, this model aids in determining the appropriate MST for all subassemblies of wind turbines to achieve the necessary degree of availability.

Numerical investigation of rotational speed effects on flow separation and boundary layer dynamics in ducted wind turbines

In this paper, we conduct a comprehensive numerical investigation of a ducted wind turbine under various rotational speeds, focusing on aspects such as boundary layer thickness and momentum thickness. Our study utilizes advanced computational fluid dynamics techniques to assess the intricate flow characteristics and aerodynamics of the ducted wind turbine, providing a detailed understanding of its operation under different speed conditions. The primary objectives are to comprehend the effect of the wind turbine’s rotational speed on the boundary layer thickness and the momentum thickness, two crucial parameters influencing the turbine’s performance and efficiency. Our findings reveal a significant correlation between the increase in rotational speed and the growth of flow separation, a phenomenon typically indicated by an expansion in the area of negative wall shear stress. This research contributes valuable insights into the behavior of ducted wind turbines at varying rotational speeds, and the acquired knowledge could potentially be utilized in designing and improving wind turbine systems to achieve better performance and enhanced operational efficiency.

Reasons for the Recent Onshore Wind Capacity Factor Increase

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected.

A new resonant fault current limiter for improved wind turbine transient stability

This paper proposes a new resonance-type FCL, which is designed specifically for DFIG-based wind turbines. The proposed topology overcomes the well-documented drawbacks associated with conventional resonance-based FCLs while preserving the advantages of this topology. The proposed circuit limits the fault current for the entire fault period independently of the reactor's charging state and significantly reduces the wind turbine's torque oscillations during a fault. The proposed FCL is simulated as part of a power system that includes a wind turbine, synchronous generator, and two step-up transformers. The results show that during a three-phase to-ground fault, the proposed FCL significantly improves the system's stability, and leads to improved fault current, voltage, active power, reactive power, and torque transients.