Performance Of Wind Turbine Research Articles

Mechanism of bionic leading-edge protuberances on the aerodynamic performance of horizontal axis wind turbine

Mechanism of bionic leading-edge protuberances on the aerodynamic performance of horizontal axis wind turbine

Enhancing darrieus wind turbine performance through varied plain flap configurations for the solar and wind tree

Plain flaps (PFs) significantly increase camber, enhancing lift and aerodynamic performance when deployed. In Darrieus Vertical Axis Wind Turbines (VAWTs), which perform efficiently in low-speed, turbulent wind conditions, structural modifications like PFs can improve efficiency. This study explores plain flaps with 10-20-degree deflections at different chord lengths to enhance the NACA 2412 aerofoil’s performance. Using Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations and the Shear Stress Transport (SST) k-ω turbulence model, simulations were conducted across high (Re ≈ 2.71 × 105), medium (Re ≈ 1.35 × 105), and low (Re ≈ 5.4 × 104) Reynolds numbers (Re). The 0.7–10 and 0.8–10 configurations significantly improved torque and Cp. At a Tip Speed Ratio (TSR) of 2.5, the 0.8–10 configuration increased the Cp by 19.51% over the flapless NACA 2412 without PF. The 0.7–10 configuration achieved the highest Cp across all TSRs, while a three-blade setup improved Cp by 43% compared to four- and five-blade configurations. The modified blades demonstrated consistent torque gains across all Re, proving the effectiveness of blade shape modifications in enhancing VAWT efficiency, particularly under fluctuating wind conditions, indicating the potential of PF-modified blades in improving small-scale wind turbine performance in varied urban environments.

Aerodynamic performance enhancement of a vertical-axis wind turbine by a biomimetic flap.

We improve the aerodynamic performance of a simplified vertical-axis wind turbine (VAWT) using a biomimetic flap, inspired by the movement of secondary feathers of a bird's wing at landing (Liebe 1979). The VAWT considered has three NACA0018 straight blades at the Reynolds number of 80000 based on the turbine diameter and free-stream velocity. The biomimetic flap is made of a rigid rectangular curved plate, and its streamwise length is 0.2cand axial (spanwise) length is the same as that of blade, wherecis the blade chord length. This device is installed on the inner surface of each blade. Its one side is attached near the blade leading edge (pivot point), and the other side automatically rotates around the pivot point (without external power input) in response to the surrounding flow field during blade rotation. The flap increases the time-averaged power coefficient by 88% at the tip-speed ratio of 0.8, when its pivot point is at 0.1cdownstream from the blade leading edge. While the torque on the blade itself does not change even in the presence of the flap, the flap itself generates additional torque, thus increasing the overall power coefficient. The phase analysis indicates that the power coefficient of VAWT significantly increases during flap opening to full deployment through the interaction with vortices separated from the blade leading edge. When the pivot point of flap is farther downstream from the leading edge or the flap operates at a high tip-speed ratio, the performance of the flap diminishes due to its weaker interaction with the separating vortices.

Abnormal Sound Detection of Wind Turbine Gearboxes Based on Improved MobileFaceNet and Feature Fusion

To solve problems such as the unstable detection performance of the sound anomaly detection of wind turbine gearboxes when only normal data are used for training, and the poor detection performance caused by the poor classification of samples with high similarity, this paper proposes a self-supervised wind turbine gearbox sound anomaly detection algorithm that fuses time-domain features and Mel spectrograms, improves the MobileFaceNet (MFN) model, and combines the Gaussian Mixture Model (GMM). This method compensates for the abnormal information lost in Mel spectrogram features through feature fusion and introduces a style attention mechanism (SRM) in MFN to enhance the expression of features, improving the accuracy and stability of the abnormal sound detection model. For the wind turbine gearbox sound dataset of a certain wind farm in Guangyuan, the average AUC of the sound data at five measuring point positions of the wind turbine gearbox using the method proposed in this paper, STgram-MFN-SRM, reached 96.16%. Compared with the traditional anomaly detection methods LogMel-MFN, STgram-MFN, STgram-Resnet50, and STgram-MFN-SRM(CE), the average AUC of sound detection at the five measuring point positions increased by 5.19%, 4.73%, 11.06%, and 2.88%, respectively. Therefore, the method proposed in this paper effectively improves the performance of the sound anomaly detection model of wind turbine gearboxes and has important engineering value for the healthy operation and maintenance of wind turbines.

Power regulation of a wind farm through flexible operation of turbines using predictive control

Wind farms are becoming larger, presenting opportunities to improve their efficiency and effectiveness. Although wind farm control can be used to adjust a wind farm’s power output to meet grid requirements, its development is restricted by the limited flexibility of wind turbine operation. Wind turbines are typically designed to operate at the power output determined by the wind speed, making it difficult to adjust their power output quickly and easily in response to changes in grid demand. A new approach to wind farm control that provides full flexibility for both wind farms and turbines is proposed. This method adjusts the wind farm’s power production using predictive control of each turbine to meet the grid demands. The power generated by each turbine is adjusted to achieve this, keeping fluctuation in the wind farm power output low. A wind farm simulation is conducted under varying wind speeds using the discretized C MEX Matlab/SIMULINKR○ model of the 5 MW Supergen turbine and the wind turbine model from NREL. The results demonstrate that the wind farm power output tracks a reference value that can be set by the grid. The results are also analyzed on the torque/speed plane, and they indicate that the turbines operate within their specified safe operating zones under both normal and gusty wind operating conditions at the same time.

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.

Centrifugal Test Study on the Vertical Uplift Capacity of Single-Cylinder Foundation in High-Sensitivity Marine Soil

Offshore wind power is a new type of clean energy with broad development prospects. Accurate analysis of the uplift capacity of offshore wind turbine foundations is a crucial prerequisite for ensuring the safe operation of wind turbines under complex hydrodynamic conditions. However, current research on the uplift capacity of suction caissons often neglects the high-sensitivity characteristics of marine soils. Therefore, this paper first employs the freeze–thaw cycling procedure to prepare high-sensitivity saturated clay. Subsequently, a single−tube foundation for wind turbines is constructed within a centrifuge through a penetration approach. Ten sets of centrifuge model tests with vertical cyclic pullout are conducted. Through comparative analysis, this study explores the pullout capacity and its variation patterns of suction caisson foundations in clay with different sensitivities under cyclic loading. This research indicates the following: (1) The preparation of high-sensitivity soil through the freeze−thaw procedure is reliable; (2) the uplift capacity of suction caissons in high−sensitivity soil rapidly decreases with increasing numbers of cyclic loads and then tends to stabilize. The cumulative displacement rate of suction caissons in high-sensitivity soil is fast, and the total number of pressure–pullout cycles required to reach non-cumulative displacement is significantly smaller than that in low-sensitivity soil; (3) the vertical cyclic loading times and stiffness evolution patterns of single-tube foundations, considering the influence of sensitivity, have been analyzed. It was found that the secant stiffness exhibits a logarithmic function relationship with both the number of cycles and sensitivity. The findings of this study provide assistance and support for the design of suction caissons in high-sensitivity soils.

Dynamic performance assessment of offshore wind structures based on root morphology model

Assessing the dynamic performance of offshore wind turbines (OWTs) is crucial for their safe operation and maintenance. To evaluate the structural dynamic performance, traditional methods rely on the reanalysis of damage models, leading to the lack of the real-time capability. Therefore, this paper presents a structural dynamic performance assessment technology based on the root morphology model to address such issue. The approach is developed to synergize data fusion-based and environmental condition classification-based dynamic performance assessment technologies. The data fusion-based method, using optimized VMD for denoising and ARMA models for free response signals, enables real-time dynamic performance assessment by tracking changes in AR coefficients over time. In parallel, the environmental condition classification-based method employs wind speed and wave height monitoring data to classify structural response data into multiple healthy sub-databases. Subsequently, Extenics is applied to compute the correlation between the data to be evaluated and the performance state levels based on the classified data, providing a precise evaluation of the structural dynamic performance. Finally, these two technologies are integrated through root morphology models, which fully consider structural vibration response data and environmental monitoring data. To verify the real-time and reliability of the proposed method, field study has been carried out on a 4 MW monopile OWT during the passage of Typhoon 'In-fa'. Results indicate that the root morphology model-based assessment technique effectively evaluates the impact of typhoons on the dynamic performance of OWTs. Further, a rose diagram for the dynamic performance assessment of OWTs is drawn to achieve real-time evaluation of OWT dynamic performance, which has certain engineering significance to guarantee the safe operation of OWTs and reduce the cost of operation and maintenance.

Evaluation of aging characteristics in wind turbine performance based on yaw power loss

The yaw dynamics of wind turbines are crucial for ensuring their operational efficiency and maximizing wind energy capture. However, excessive yaw movements may precipitate premature aging of these turbines. Investigating how yaw behavior influences turbine performance can aid in refining yaw control strategies, thereby mitigating the rate of performance degradation. This paper analyzes five years of SCADA data from a wind farm, employs the DBSCAN algorithm to process anomalous data, and explores the correlation between state parameters and power output under varying operational conditions. The study leverages kernel density estimation and least squares approximation for univariate data processing and curve fitting. Furthermore, it introduces the concept of a yaw loss rate to assess power efficiency during yaw maneuvers quantitatively, calculates yaw-induced power losses under diverse conditions, and proposes a novel method to evaluate turbine performance by considering historical trends in power capture. The findings confirm that the proposed evaluation methodology is practical and effective, substantiated by analyzing five consecutive years of SCADA data from four turbines located in a mountainous wind farm in southern China.

CFD Investigation of the Effect of Multiple Phase Shift Angles on the Performance of a Two-Stage Savonius Wind Turbine

CFD Investigation of the Effect of Multiple Phase Shift Angles on the Performance of a Two-Stage Savonius Wind Turbine

Dynamic Gust Detection and Conditional Sequence Modeling for Ultra-Short-Term Wind Speed Prediction

As the foundation for optimizing wind turbine operations and ensuring energy stability, wind speed forecasting directly impacts the safe operation of the power grid, the rationality of grid planning, and the balance of supply and demand. Furthermore, gust events, characterized by sudden and rapid wind speed fluctuations, pose significant challenges for ultra-short-term wind speed forecasting, making the data more complex and thus harder to predict accurately. To address this issue, this paper proposes a novel hybrid model that combines dynamic gust detection with Conditional Long Short-Term Memory (Conditional LSTM) and incorporates dynamic window adjustment and wind speed difference threshold screening methods. The model dynamically adjusts the window size to accurately detect gust events and uses a conditional LSTM model to adjust predictions based on gust and non-gust conditions. Experimental results show that the proposed model exhibits higher prediction accuracy across various wind speed scenarios, particularly during gust events. Through detailed experiments using data from a single actual wind farm, the effectiveness and practicality of the proposed hybrid model are demonstrated. The experimental results indicate that the proposed model outperforms contrast models, especially in handling gust events, significantly enhancing the robustness of ultra-short-term wind speed predictions.

Trend-constrained pairing based incremental transfer learning for remaining useful life prediction of bearings in wind turbines

Accurate remaining useful life prediction of bearings plays a significant role in safe operation of wind turbines. However, due to the harsh working condition, bearings often present complex and variable degradation trajectories, which leaving a challenge to guide the prediction by drawing on the historical experience with distribution differences, or even of cross equipment and cross conditions. To resolve this challenge, a novel trend-constrained incremental transfer prognosis (TCITP) method is proposed in this paper. Firstly, a feature fusion-based health indicator construction method is presented for a monotonic and smooth health indicator based on a novel monotonicity metric and a degradation increment cumulation algorithm. Secondly, a trend-constrained transfer learning method is designed for state estimation on the basis of a cross-domain transfer pairing algorithm. Thirdly, an incremental prognosis method is developed to update the prognostic model for remaining useful life prediction. Several bearings from accelerated degradation platforms and on-site wind turbine high-speed shafts are utilized to demonstrate the effectiveness and superiority of the proposed model. The error of the prediction is less than 3.5 samplings in last 10 samplings, and is also less than 5 samplings in last 20 samplings, which is able to provide advisable maintenance schedule to the on-site wind farm.

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.

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.

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

ABSTRACTWith increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI‐VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie—Artificial Intelligence‐Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so‐called thermal turbulence patterns (TTPs) that result from such surface contamination or damage. To complement the thermographic inspections, high‐resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.

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.