Performance Of Wind Turbine Research Articles

Renewable-driven hybrid refrigeration system for enhancing food preservation: Digital twin design and performance assessment

This study presents a new method for sustainable cooling systems using a hybrid refrigeration system powered by hybrid renewable energy sources. The system comprises a modular unit of vertical wind turbines integrated with bio-photovoltaic films to provide sustainable energy. The hybrid refrigeration system combines evaporative and solar thermal-driven adsorption cooling systems. In addition, a finite volume of soil is proposed for thermal energy storage. Experimental data inform the development of a digital twin for an integrated system, soil thermophysical characteristics, wind turbine performance, and technical specifications for other system components. This sustainable cooling package is cost-effective and space-efficient, particularly in remote or off-grid locations. Notably, the evaporative cooler and chilled water coil contribute to a cooling effect of 20.4 kW, and solar power generation reaches 12.38 kW at an intensity of 1053 W/m2. The annual electrical output averages 1.7 kW at a wind speed of 3.5 m/s. Under best conditions, wind power can surge to 7.99 kW at 9.88 m/s. The ratio of power generated by wind to solar energy ranges from 1.1 to 1.3. The system effectively meets a peak thermal energy demand of approximately 74 GJ/month, facilitated by solar collectors, underground thermal storage, and a renewable energy-fed auxiliary heater. This study paves the way for future techno-economic optimisation and advancements in sustainable energy solutions for remote cold storage facilities.

Wingsail performance in unsteady atmospheric surface layer winds

The performance of wind propulsion systems is evaluated in unsteady inhomogeneous winds with tools that are routinely used to predict the performance of wind turbines and which give access to the unsteady aerodynamic forces acting on the wingsail. A rigid wingsail exposed to a realistic atmospheric surface layer wind is used as a testbed. For this case, we show that standard deviations of the aerodynamic driving force are larger than 15%–20% of the mean values when the true wind velocity is larger than the ship speed. We also show that the mean aerodynamic driving forces computed by averaging the unsteady driving forces are only slightly smaller than the ones computed on the mean wind despite the strongly nonlinear dependence of the unsteady forces on the wind velocity and direction.

Numerical investigation of unsteady aerodynamics on a grooved NACA 4415 airfoil

Surface grooves have demonstrated the capability to mitigate laminar separation bubbles and enhance airfoil aerodynamic performance. However, wind turbine operation introduces dynamic effects that significantly impact airfoil behavior, leading to substantial deviations from static conditions. Numerical simulations were carried out to discuss the effectiveness of surface groove structures in controlling separation bubbles under dynamic conditions and their potential to enhance unsteady aerodynamics. The influence of groove parameters (depth, width, position) on the NACA 4415 airfoil’s boundary layer attributes and unsteady aerodynamic properties are analyzed across various pitching amplitudes and reduced frequencies. It is found that the position of groove structures under dynamic conditions is crucial, that groove structures located ahead of separation bubbles on the airfoil surface can effectively eliminate the separation bubbles, and their control effectiveness is superior to that under static conditions. Well-designed groove structures can reduce the airfoil’s maximum lift, drag, and moment coefficients under dynamic conditions. They also reduce the aerodynamic hysteresis effect and improve the aerodynamic damping characteristics. This comprehensive analysis sheds light on the critical interplay between dynamic wind conditions, airfoil behavior, and the potential of groove structures to optimize wind turbine performance.

Floating offshore wind turbine nonlinear model predictive control optimisation method

This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.

CFD Analysis on Novel Vertical Axis Wind Turbine (VAWT)

The operation of vertical axis wind turbines (VAWTs) to generate low-carbon electricity is growing in popularity. Their advantages over the widely used horizontal axis wind turbine (HAWT) include their low tip speed, which reduces noise, and their cost-effective installation and maintenance. A Farrah turbine equipped with 12 blades was designed to enhance performance and was recently the subject of experimental investigation. However, little research has been focused on turbine configurations with more than three blades. The objective of this study is to employ numerical methods to analyse the performance of the Farrah wind turbine and to validate the findings in comparison with experimental results. The investigated blade pitch angles included both positive and negative angles of 7, 15, 20 and 40 degrees. The k-ω SST model with the sliding mesh technique was used to perform simulations of a 14.4 million element unstructured mesh. Comparable trends of power output results in the experimental investigation were obtained and the assumptions of mechanical losses discussed. Wake recovery was determined at an approximate distance of nine times the turbine diameter. Two large complex quasi-symmetric vortical structures were observed between positive and negative blade pitch angles, located in the near wake region of the turbine and remaining present throughout its rotation. It is demonstrated that a number of recognised vortical structures are transferred towards the wake region, further contributing to its formation. Additional notable vortical formations are examined, along with a recirculation zone located in the turbine’s core, which is described to exhibit quasi-symmetric behaviour between positive and negative rotations.

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

Experimental Study on Identification of Aerodynamic Damping Matrix for the Tower of an Operating Wind Turbine by Artificial Excitation

ABSTRACTQuantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors' previous theoretical and numerical studies showed that compared with damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore‐aft (FA) and side‐side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5‐MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady‐state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice.

Infrared Thermography of Turbulence Patterns of Operational Wind Turbine Rotor Blades Supported With High‐Resolution Photography: KI‐VISIR Dataset

Infrared Thermography of Turbulence Patterns of Operational Wind Turbine Rotor Blades Supported With High‐Resolution Photography: KI‐VISIR Dataset

Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance

Wind energy is a significant renewable resource, but its efficient harnessing requires advanced control systems. This study presents a Data-Centric Predictive Control (DPC) system, enhanced by a Tuna Swarm Optimization-Backpropagation Neural Network (TSO-BPNN) for predictive wind turbine control. It's like a smart tool that uses innovative fusion of deep learning, predictive Control, and reinforcement learning. Unlike traditional control methods, the proposed approach uses real-time data to optimize turbine performance in response to fluctuating wind conditions.The system is validated using simulations on the FAST platform, which demonstrate its superior performance in two critical operational regions. Specifically, in Region II, where the objective is to maximize power extraction from the wind, the DPC achieves a 1.07 % reduction in overshoot and an improvement of 36.14 units in steady-state error compared to traditional methods. The response time remains comparable to existing Model Predictive Control (MPC) strategies, ensuring real-time applicability without sacrificing efficiency. In Region III, where maintaining constant power output is crucial, the DPC outperforms both the baseline and MPC methods, reducing overshoot by 0.58 % and improving accuracy by 17.27 units compared to the baseline method. These results highlight the effectiveness of the proposed DPC system in optimizing turbine performance under variable wind conditions, offering a significant improvement over traditional methods in both accuracy and control precision.

Experimental study and finite element analysis on shear performance of offshore wind turbine jacket foundation

The repeated extrusion of sea ice leads to the continuous collapse of marine structures. In this paper, the shear test and finite element simulation analysis of jacket foundation of large offshore wind turbine are carried out. The results show that the bearing capacity of the structure under positive displacement loading is slightly larger than that under negative displacement loading, and the bearing capacity is increased by 5.3 %. The lateral support joints have compression bending and fracture failure modes, and the maximum strain is 3600 με. Finally, the failure mode, energy dissipation capacity and bearing capacity of jacket foundation under reciprocating displacement load are obtained. At the same time, the ice load action model is established, and the research results provide experimental basis for improving the shear performance of offshore wind turbine.

Simulation of unsteady ice accretion on horizontal axis wind turbine blade sections in turbulent wind shear condition

This study presents a comprehensive simulation approach to quantify power losses in horizontal axis wind turbines under environmental icing conditions. It investigates how wind shear and turbulence affect a 2.5 MW wind turbine's performance, particularly under ice accretion. Turbulence intensity, ranging from 1% to 20%, impacts the relative flow fluctuations and angle of attack on the blade sections, influencing the aerodynamic penalty ratio. The incoming wind speed and the flow angle at various blade sections were determined using the unsteady blade element momentum method, considering vortex induction effects and Prandtl and Glauert corrections. For ice accretion analysis, a fully unsteady simulation of computational grid motion due to ice accretion was performed, along with the solution of the multiphase flow of water dispersed particles in cold air, derived from the psychrometric chart. The findings highlight the significant impact of the incoming turbulent wind fluctuations on the dispersion of the ice shape formed at sections corresponding to their radial position on the blade according to the momentary angle of attack fluctuations. The formation of ice profiles along the blade has led to a subsequent degradation in the aerodynamic efficiency of the blade sections, which is directly proportional to the escalation in turbulence intensity. This phenomenon leads to a continual reduction in the power output of the wind turbine. This research provides valuable insights into the performance of wind turbines under icing conditions in real wind fluctuations.

Effect of roof fence shape on flow over a building and its impact on wind turbine performance

Effect of roof fence shape on flow over a building and its impact on wind turbine performance

Novel passive flow control method using leading-edge prism-shaped cylinder: Performance enhancement of vertical-axis wind turbines

Due to periodic dynamic stall at low tip speed ratios (TSRs), vertical-axis wind turbines (VAWTs) experience notable performance challenges during rotation, which leads to fluctuations in torque and a decrease in energy capture. This research aims to boost the aerodynamic performance of Darrieus VAWTs by employing a leading-edge (LE) prism cylinder (PC) to enhance energy extraction. This novel small-scale device functions as a passive method for controlling flow separation, aiming to energize the boundary layer and adjust the pressure distribution on the blades. Its effectiveness depends on factors such as size, shape, and placement, necessitating careful optimization. A three-dimensional (3D) computational fluid dynamics (CFD) analysis, combined with Taguchi optimization and analysis of variance, is conducted to determine the optimal design parameters for the LE PC tool. This 3D CFD method captures the full complexity of flow dynamics, including vortex structures and wake behavior, leading to more accurate wind turbine performance predictions than two-dimensional (2D) CFD models. The results highlight the crucial role of PC size (Factor A), which contributes nearly 85% to the total contribution factor, while the angle of PC influence is minimal. The optimized rotor demonstrates a 36% increase in maximum average power coefficient (CP) compared to an uncontrolled rotor at TSR = 1.5. However, the effectiveness of this control method diminishes at higher TSRs because the blades encounter angles of attack below the critical stall angle throughout the rotation cycle, naturally preventing flow separation and making the flow separation control method unnecessary. The PC installed on the optimized blade delays flow separation to 55% of the blade chord length, compared to 40% for the base blade. Consequently, the rotor operates efficiently, ensuring consistent, and reliable power generation without flow separation issues.

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.

Computational Fluid Dynamics (CFD) Evaluation of a Horizontal-axis Wind Turbine with a Bionic Blade

With the world's energy supply becoming increasingly scarce, wind power is gaining popularity as a clean source of renewable energy which is good for the environment. Thus, it is an alternative to burning fossil fuels such as coal, which may have an environmental impact during operations due to the emissions of unwanted gases. Wind turbines are the devices used to produce power by moving a turbine's propeller-like blades around a rotor, which spins a generator that generates energy as air passes through them. However, existing wind turbines still suffer from low conversion efficiencies. Therefore, in this current study, the bionic blade design has been used to enhance the performance of a horizontal axis wind turbine utilizing a computational simulation approach while seeking to improve its efficiency. The blade profile of the model used was based on the NACA 0012 airfoil with the modified angle of attack. The hybrid shear stress transport (SST) k-omega was used as the turbulence model. The computational procedures involved the use of various inlet velocities while maintaining a constant rotational speed. The results show that the bionic design was found to improve the overall performance of the standard NACA0012 blade design. Moreover, both power and torque coefficient outputs increase as the tip speed ratio (TSR) increases. However, the torque decreases as the TSR increases. In terms of power coefficient, the highest conversion efficiency was about 28% and it was achieved at 3.7 TSR.

Simulation of Small-Scale Wind Turbine Performance with Taperless Blade

Utilizing wind turbines typically involves a large-scale project with extremely huge wind turbine sizes. Because of this, the use of wind turbines in Indonesia is not yet very common and widespread among the community. Basically, a wind turbine has blades; the number of blades on a wind turbine can affect the overall performance of the turbine. Large-scale wind turbine projects usually implement tapered blades, while small-scale projects use taperless blades. Therefore, in this research, a horizontal-axis wind turbine with taperless blades will be used as the object of study to find out the optimal number of taperless blades that can achieve the best performance of a small-scale wind turbine category. The method used in this research is to design a horizontal axis wind turbine equipped with taperless blades with varying numbers of blades, namely three blades, four blades, and five blades, and then simulate the turbine's performance with wind speeds of 3 m/s to 6 m/s. The present research found that the greater the number of turbine blades tends to produce greater power. This is proven by research results, where the highest power in a small-scale horizontal axis wind turbine produces a power of 20,30 Watts in a 5-blade turbine variation for the wind speed of 6 m/s.

Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences

Abstract. This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy.

Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench

Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

Ensuring Stable Operation of Wind Farms Connected to Distribution Networks

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes.