Advanced Wind Turbine Research Articles

Comparative analysis of carbon fibre and glass fibre in blade design

This study investigates the performance, environmental impact, and economic viability of single-blade carbon fibre wind turbines compared to traditional three-blade glass fibre designs. Finite Element Analysis (FEA) demonstrates carbon fibre's superior stiffness and vibration properties, while Computational Fluid Dynamics (CFD) simulations identify optimal aerodynamic performance at specific angles of attack. A Life Cycle Assessment (LCA) reveals that, despite significantly higher carbon emissions and energy consumption during manufacturing, carbon fibre blades produce fewer SO₂-equivalent emissions. Economically, single-blade carbon fibre turbines present potential cost-efficiency due to reduced maintenance and extended lifespan. However, challenges such as manufacturing energy demands, environmental effects, and cost remain barriers to widespread adoption. These findings underscore carbon fibre’s suitability for applications requiring high mechanical performance and dimensional stability, establishing it as a viable material for advanced wind turbine designs.

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Selective recovery of rare earth elements by smelting of magnets

Rare earth elements (REEs) play a crucial role in many technologies from daily appliances in cell phones to more advanced wind turbines and electric cars. Permanent magnets account for a quarter of total global REEs production and have high recycling value. In this study, smelting process was used to selectively oxidize REEs in the permanent magnets by adding Fe2O3. This separates REEs into a slag phase from an iron-rich metallic phase. B2O3 was also added to the system as a flux to lower the slag melting temperature. This minimizes REEs loss to the metallic phase and allows a more efficient phase separation. The effect of flux and oxidizing agent addition was investigated on both regular and cerium-rich NdFeB (NdCeFeB) magnets. At 1350 °C and for 1 h, the slag phase was successfully separated from the metallic phase with the addition of 0.8 stoichiometric amount of Fe2O3 and 40 wt% of B2O3. Scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM-EDX) analysis reveals that REEs in the magnet do not migrate to the metal phase while the REE-rich slag phase contains almost no iron. After the selective removal of iron into the metallic phase, REEs are recovered from the slag phase through an acid leaching process allowing >99% of REEs recovery. Boron in the magnet can also be recovered as useful boric acid by evaporation and crystallisation technique. The proposed process in this study is reagent and energy-efficient with almost complete valorisation of both NdCeFeB and NdFeB magnets.

Integrating High Levels of Wind in Island Systems: Lessons from Hawaii

such as wind and solar plants presents an operational challenge for grid operators. The economic incentives and technical challenges that accompany large amounts of variable generation in island power systems are often much greater. The Hawaiian Electric Company and its subsidiaries, the Maui Electric Company and the Hawaii Electric Light Company have considerable experience in planning and operating power systems with relatively high levels of wind power. The islands of Hawaii and Maui operate power systems with high levels of wind power (more than 10% by energy) and have experienced and addressed challenges associated with the variability and uncertainty of wind power. The island of Maui is anticipating further wind plant deployments in the near future. Recent analyses of possible near-term deployment of large amounts of wind power on the Oahu power system (500MW of wind power; approximately 1200MW peak and 520MW minimum annual load) has shown the potential for this system to accept almost 25% of its energy from wind and solar power. This paper will identify some of the wind integration challenges and highlight the benefits of a variety of strategies that are expected to improve system economics and operational reliability, including proposed modifications to the baseload thermal fleet (deeper turndown, higher ramp rates, and tuned droop characteristics), advanced wind turbine grid support features, new operating strategies, wind forecasting and refinements to the up and down reserve requirements. This paper will present the key findings in the context of useful insights and lessons learned that are relevant to other island power systems considering very high levels of wind power.

Novel Deep Learning Model for Predicting Wind Velocity and Power Estimation in Advanced INVELOX Wind Turbines

Wind energy is a renewable energy source that has grown rapidly in recent decades. This energy is converted into electricity using advanced INVELOX wind turbines. However, the wind velocity is critical, and predicting this velocity in real-time is challenging. As a result, a deep learning (DL) model has been developed to predict the velocity in advanced wind turbines using a novel enhanced Long Short-Term Memory (LSTM) model. The LSTM enhancement is executed by employing the Black Widow optimization with Mayfly optimization in the Python platform as application software. The dataset has been prepared using Ansys Fluent fluid flow analysis. In addition to that, the wind turbine power generation was computed analytically. A subsonic wind tunnel test is also performed by employing a 3-Dimensional printed physical model to validate the simulation dataset for this innovative design. The proposed MFBW-LSTM model (Enhanced LSTM with BWO and MFO) predicts efficiently, with an accuracy of 95.34%. Furthermore, the performance of the proposed model is compared to LSTM, BW-LSTM, and MF-LSTM. Accuracy, MAE, MAPE, MSE, and RMSE are among the performance criteria the proposed DL model achieves efficiently. As a result, the proposed DL model is best suited for velocity prediction of an Advanced INVELOX wind turbine in various cross sections with high accuracy.

Investigation of Aeroelastic Energy Extraction from Cantilever Structures under Sustained Oscillations

The present article is focused on a detailed computationalinvestigation of energy production capacity of various lightweight materials that are employed with piezoelectric vibration energy harvesters (PVEHs) subjected to various aeroelastic effects. Piezoelectric transducers are primarily employed to capture vibrational energy, which yields predictable and locally storable electrical energy. Higher energy extraction is possible under larger deflections of the structures when they are employed with PVEHs. In order to estimate the largest possible deflection of the structures, the response of them under external perturbations is estimated. An airplane wing consists of tapered planform, an advanced wind turbine blade, and the rectangular wings of an unmanned aerial vehicle (UAV) are considered for the vibrational analysis as the feasibility of achieving larger deflection is high compared with other aerodynamic surfaces. The stated elastic structures are modelled with different lightweight materials such as aluminium alloy, glass fibre-reinforced polymer (GFRP), titanium alloy, carbon fibre-reinforced polymer (CFRP), and Kevlar fibre-reinforced polymer (KFRP). Advanced partly coupled computational simulations are carried out with computational fluid dynamics (CFDs), and structural and vibrational effects to investigate the energy harvesting potential from the perturbations. Based on the outcomes of vibrational analysis, the raw transformable power production capacity of different lightweight materials that are employed with a cantilevered PVEH is estimated. The most suitable combination of material and associated aeroelastic effect which yields a significant amount of raw energy in each application is proposed and discussed with findings.

Sensor fault-tolerant control for wind turbines: an iterative learning method

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Topology Optimization-Driven Design for Offshore Composite Wind Turbine Blades

With the increase in wind turbine power, the size of the blades is significantly increasing to over 100 m. It is becoming more and more important to optimize the design for the internal layout of large-scale offshore composite wind turbine blades to meet the structural safety requirements while improving the blade power generation efficiency and achieving light weight. In this work, the full-scale internal layout of an NREL 5 MW offshore composite wind turbine blade is elaborately designed via the topology optimization method. The aerodynamic wind loads of the blades were first simulated based on the computational fluid dynamics. Afterwards, the variable density topology optimization method was adopted to perform the internal structure design of the blade. Then, the first and second generation multi-web internal layouts of the blade were reversely designed and evaluated in accordance with the stress level, maximum displacement of blade tip and fatigue life. In contrast with the reference blade, the overall weight of the optimized blade was reduced by 9.88% with the requirements of stress and fatigue life, indicating a better power efficiency. Finally, the vibration modal and full life cycle of the designed blade were analyzed. The design conception and new architecture could be useful for the improvement of advanced wind turbines.

Structural Behavior Analysis of UHPC Hybrid Tower for 3-MW Super Tall Wind Turbine Under Rated Wind Load

Based on the conceptual design of an advanced wind turbine tower system, use of ultra-high-performance cementitious composites material with compressive strength of 200 MPa (UHPC-200) is proposed to ensure high durability and ductility of the UHPC hybrid wind turbine tower. Key design parameters are proposed for the structural design of a 3-MW wind turbine. The material properties, mixing compositions, simplified constitutive relationship, and model parameters are outlined. Using nonlinear finite element analysis, the effects of wall thickness, wall thickness ratio, and prestressing tendon on the structural performance including the longitudinal stress field, lateral displacement, stress concentration at the transition zone between the middle and bottom segments are evaluated. Based on the stress-field analysis, the design limitation of the segmental wall thickness and its ratio is recommended. The numerical results show that the tower with the wall thickness ratio of 2:3 (i.e., thickness 200–300 mm) with prestressing tendons is an optimal design for the UHPC hybrid tower. The results of this study can be used as a reference for the engineering design of a new type of UHPC hybrid wind turbine tower.

Structur Structural Dynamics of A al Dynamics of AWT-27 Wind T -27 Wind Turbine Blade urbine Blade

Wind energy is one of the world’s current leading renewable energy resources. One of the major aspects of studying wind turbines is the structural dynamics for the turbine structure including blades and support structure. In the current work, the blades of the Advanced Wind Turbine (AWT-27) are investigated in a dynamic approach. Different wind fields have been generated for the study to provide different Design Load Conditions (DLCs). Three laminar wind velocities of 5 m/s, 12 m/s, and 17 m/s were simulated. Turbulent wind flow fields have also been generated at the three standard classes A, B and C of high, medium, and low turbulence intensities respectively. The generated wind fields were used as inputs to calculate the aerodynamic loads for each wind condition using the Blade Element Momentum (BEM) theory. Aerodynamic loads have been calculated, namely, the shear force on five different locations along the blade length. Results of the simulation are summarized such that the shear forces at the blade root, 30%, 50%, 70% of the blade length are known for each wind condition. The summary serves as a guide for further optimization of the blade structural design.

Towards the development of an advanced wind turbine rotor design tool integrating full CFD and FEM

Large, highly flexible wind turbines of the new generation will make designers face un-precedent challenges, mainly connected to their huge dimensions. To tackle these challenges, it is commonly acknowledged that design tools must evolve in the direction of both improving their accuracy and turning into holistic, multiphysics tools. Furthermore, the wind turbine industry is reaching a high level of maturity, and ever more accurate and reliable design tools are required to further optimise these machines. Within this framework, the study shows the development of an integrated platform for blade design integrating 3D CFD flow simulations and 3D-FEM structural analysis. Artificial intelligence techniques are applied to develop an optimization procedure based on the proposed tool. The potential of the new platform has been tested on the well-known test case of the MEXICO rotor, for which an optimization of the blade design has been carried out. Exploring a design space sampled with 2000 CFD and FEM computations, increases in blade torque have been obtained at each of the three tip-speed ratios (TSR) investigated, ranging from 6% at the nominal TSR to 14% at the lowest one, while stresses on the blade are kept almost unaltered.

Electrical and Mechanical Characteristics Assessment of Wind Turbine System Employing Acoustic Sensors and Matrix Converter

Permanent magnet synchronous generator (PMSG)-based wind turbine systems have a wide range of applications, notably, for higher-rated wind energy conversion systems (WECS). A WECS involves integrating several components to generate electrical power effectively on a large scale due to the advanced wind turbine model. However, it offers several glitches during operation due to various factors, notably, mechanical and electrical stresses. This work focuses on evaluating the mechanical and electrical characteristics of the WECS using two individual schemes. Firstly, wind turbines were examined to assess the vibrational signatures of the drive train components for different wind speed profiles. To apply this need, acoustic sensors were employed that record the vibration signals. However, due to substantial environmental impacts, several noises are logged with the observed signal from sensors. Therefore, this work adapted the acoustic signal and empirical wavelet transform (EWT) to assess the vibration frequency and magnitude to avoid mechanical failures. Further, a matrix converter (MC) with input filters was employed to enhance the efficiency of the system with reduced harmonic contents injected into the grid. The simulated results reveal that the efficiency of the matrix converter with input filter attained a significant scale of about 95.75% and outperformed the other existing converting techniques. Moreover, the total harmonic distortion (THD) for voltage and current were examined and found to be at least about 8.24% and 3.16%, respectively. Furthermore, the frequency and magnitude of the vibration signals show a minimum scale for low wind speed profile and higher range for medium wind profile rather than higher wind profile. Consolidating these results from both mechanical and electrical characteristics, it can be perceived that the combination of these schemes improves the efficiency and quality of generated power with pre-estimation of mechanical failures using acoustic signal and EWT.

Implementation and Validation of an Advanced Wind Energy Controller in Aero-Servo-Elastic Simulations Using the Lifting Line Free Vortex Wake Model

Accurate and reproducible aeroelastic load calculations are indispensable for designing modern multi-MW wind turbines. They are also essential for assessing the load reduction capabilities of advanced wind turbine control strategies. In this paper, we contribute to this topic by introducing the TUB Controller, an advanced open-source wind turbine controller capable of performing full load calculations. It is compatible with the aeroelastic software QBlade, which features a lifting line free vortex wake aerodynamic model. The paper describes in detail the controller and includes a validation study against an established open-source controller from the literature. Both controllers show comparable performance with our chosen metrics. Furthermore, we analyze the advanced load reduction capabilities of the individual pitch control strategy included in the TUB Controller. Turbulent wind simulations with the DTU 10 MW Reference Wind Turbine featuring the individual pitch control strategy show a decrease in the out-of-plane and torsional blade root bending moment fatigue loads of 14% and 9.4% respectively compared to a baseline controller.

Unsteady-state numerical analysis of advanced Savonius wind turbine

ABSTRACT In order to improve the performance coefficients of the classic Savonius rotor, a newly-designed blade was proposed and numerically analyzed via CFD tools. For this purpose, a comprehensive-validated numerical analysis was carried out to take into account all aerodynamic aspects of the new design. The blade geometry was optimized by determining the best overlap ratio (0.47) which led to the highest performance at different tip speed ratios. The better performance of the optimal advanced Savonius rotor compared to the classic rotor was demonstrated by comparing its torque and power coefficients at different tip speed ratios. It is shown that working at relatively low tip speed ratios results in higher torque coefficients with more superiority over the corresponding classical rotor; however, to achieve more stable and regular blade rotation with less noise and fluctuations, tip speed ratios around 0.75 and smaller are proposed. In order to demonstrate the separation points, the wake phenomenon, and vortices behind the rotating blades, the pressure, velocity, and vorticity contours were prepared and analyzed.

Improving the distribution uniformity of surface hardness of the cured CFRP molniezaschita coating during processing in a microwave field elektromagnitnom

In the structural elements of advanced air transport and wind turbines are widely used fiber-reinforced polymer composite materials: carbon and fiberglass. In order to increase the resistance of materials to static electricity and lightning discharges, lightning-proof coatings are included in the structure of the polymer composite materials. In the process of operation, there may be collisions of the plumage and planes of aircraft, as well as large-sized blades of wind turbines with solid objects. Therefore, along with the strength and resistance to high-amplitude low-cycle loads, one of the important characteristics of polymer composite materials that determine their performance properties is hardness. Strength and endurance of polymer composite materials products can be increased by short-term exposure to microwave electromagnetic field. The presence of a built-in metal structure introduces additional uncertainty in the perception of anisotropic polymer composite materials operating loads, as well as in the process of interaction with the electromagnetic field of ultrahigh frequency. The studies of the hardness distribution on the surface of carbon plastics with built-in lightning-proof coatings under different schemes of exposure to microwave electromagnetic field: from the lightning-proof coatings, from the side opposite to the lightning-proof coatings, sequential processing on both sides compared with the starting material. It is found that processing in a microwave electromagnetic field with an energy flux density (17-18) ×104 mW/cm2 for 2 minutes leads to a significant increase in the uniformity of the initial hardness of the surface of the samples.

A novel blade design for Savonius wind turbine based on polynomial bezier curves for aerodynamic performance enhancement

ABSTRACT Advanced wind turbine designs and technologies have been evolved to take advantage of wind energy. Despite the significant progress already attained, the need for a dependable wind energy converter particularly devoted to small-scale applications remains a challenging issue. Due to its design simplicity, Savonius wind turbine is the most suitable candidate for such applications. It operates at low wind speed, with the necessary starting capacity and insensitivity to wind directions. Moreover, in the literature related to wind energy, the Savonius rotor is known for its low performance compared to other types of wind turbines. In this paper, we present a study into the utilization of Bézier curves and transient computational fluid dynamics (CFD) to optimize the conventional Savonius blade design. The k-ω SST turbulence model is employed to perform a series of CFD simulations in order to assess the power coefficient of each generated design. A validation of optimization results using the Taguchi method was carried out. The comparative analysis of the torque and power coefficients shows a significant increase in the power coefficient (Cp). The optimal Cp is 0.35 and is 29% higher than the conventional Savoniu wind turbine (SWT). Subsequently, the effectiveness of the innovative geometry is proved by improved pressure and velocity distributions around blades of novel design.

Manufacturing of Advanced Composite Wind Turbine Blades for Counter Rotating Vertical Wind Turbine

The paper presents the manufacturing process of advanced composite wind turbine blades designed for an experimental counter rotating vertical wind turbine (CR-VAWT). An iterative approach was used to present the manufacturing process of turbine blades starting from presentation of the turbine structure and material description as well as all manufacturing process stages. Two types of turbine blades were successfully manufactured using metallic molds and a cost-effective manufacturing technology. Based on the turbine blades obtained it can be said that the selected manufacturing process showed good results, very similar with results expected in case of using advanced technologies (i.e. autoclave technology.

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.