Performance Of Wind Turbine Research Articles

Empirical Physics‐Based Normal Behavior Modeling of Drive Train Temperatures With High‐Resolution Wind Turbine Operating Data

Empirical Physics‐Based Normal Behavior Modeling of Drive Train Temperatures With High‐Resolution Wind Turbine Operating Data

Effects of Different Blade Sweep Models and Blade Geometries on the Performance of Horizontal Axis Wind Turbines

Effects of Different Blade Sweep Models and Blade Geometries on the Performance of Horizontal Axis Wind Turbines

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective

Over the past decades, the increasing energy demand has accelerated the construction of wind farms, raising higher expectations for precise load and power assessments in wind turbine performance. Traditional methods, which rely on analytical wake models and performance curves, often fail to adapt to complex inflow scenarios, leading to significant inaccuracies in predicting turbine loads and power output. This research addresses these challenges by introducing a novel two-phase framework for various phases of wind farm planning and development, using the NREL 5MW baseline wind turbine as a case study. The first part involves deriving recommended values for simplified thrust modulation factors at the preliminary design phase, enabling swift evaluation of maximum and fatigue thrust loads crucial for wind farm optimization. The second part focuses on designing and training a machine learning model at the detailed design phase. A gradient-boosting-based framework based on LightGBM provides comprehensive methods for assessing wind turbine load and power, enhancing the precision and efficiency of these assessments. The proposed model achieves significant improvements in predictive accuracy, achieving mean R-Squared of 0.995, 0.988, and 0.995 for power, peak load, and damage equivalent load evaluation, respectively. The framework streamlines the assessment process, enhancing both the accuracy and speed of power and load evaluations for wind farm design. This is expected to reduce computational costs and improve the effectiveness of downstream tasks, such as layout optimization and wake steering.

An analysis of self-reported sleep disturbance from nighttime wind turbine noise suggests minimal effects but highlights the need for standardization in research designa).

The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data. In the current study, calculated outdoor wind turbine Lnight levels ranged from <20.5 to 41.5 dB(A). Between May and September 2013, questionnaires were completed by 606 males and 632 females, 18-79 years of age, randomly selected from households 0.25 to 11.22 km from operational wind turbines. When the source of sleep disturbance was unspecified, the mean prevalence of HSD was 13.3% overall and unrelated to Lnight (p = 0.53). As Lnight increased, identifying wind turbines as one of the causes of HSD increased from 0% below 20.5 dB(A) to 3.8% between 35.5-41.5 dB(A) (p = 0.01). The 3%HSD benchmark was observed where Lnight was 33.5 dB(A) [95% confidence interval (CI) 31.1-36.1 dB(A)]. Results affirm findings from Health Canada's Community Noise and Health Study of minimal impacts of wind turbines on sleep [Michaud et al. (2016a). "Effects of wind turbine noise on self-reported and objective measures of sleep," Sleep 39(1), 97-109], yet noted uncertainties and limitations are discussed, including the suggestion that the HSD benchmark for wind turbines may be too low.

Evaluation, Calibration, and Application of Probabilistic 100-m Wind Speed Forecasts Produced by the WRF Ensemble Prediction System in Taiwan

Abstract The use of renewable energy is on the rise; however, it brings more challenges for grid operations and unit scheduling due to the intermittent nature and limitations in predicting renewable power generation. High-quality meteorological forecasts play a critical role in the effective application and management of renewable energy resources. This study evaluates the quality and economic benefits of probabilistic forecasts for 100-m wind speeds from the Weather Research and Forecasting Model ensemble prediction system (WEPS). A multiple linear regression (MLR) method for calibration is also used to improve forecast quality. Additionally, this study explores the application of probabilistic hub-height wind speed forecasts in conjunction with an economic value analysis to determine whether wind turbines should be shut down during typhoon periods. The findings reveal that the forecast in the central offshore region of Taiwan exhibits the highest reliability and discrimination ability compared to those in the northern and southern regions. Additionally, employing the MLR calibration method significantly improved the reliability and discrimination ability of the probabilistic forecasts compared to the raw forecasts. Furthermore, during the typhoon seasons, almost all users, regardless of their cost–loss ratio, can benefit from basing their decisions on WEPS forecasts compared to using a single deterministic forecast. Importantly, decision-makers can benefit more from probabilistic forecasts than the ensemble mean forecasts when both are derived from the same ensemble prediction system, since the former incorporates forecast uncertainty. Significance Statement High-quality meteorological forecasts significantly influence the efficient utilization and management of renewable energy resources. Therefore, this study uses a simple yet effective approach to correct systematic bias in probabilistic wind speed forecasts over Taiwan, aiming to provide decision-makers with more reliable forecasts for hub-height wind speeds. Given that strong winds during typhoon periods pose safety concerns for wind turbine operation, we use an economic value analysis to assist turbine operators in deciding whether to shut down wind turbines. Furthermore, our research demonstrates that ensemble probabilistic forecasts can yield greater economic benefits for decision-makers when compared to the commonly used ensemble mean forecasts.

Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm

ABSTRACT Accurate wind energy forecasting has become increasingly important to effectively manage the energy produced by wind turbine power plants and optimize their operational performance. In this study, several artificial intelligence techniques are recommended to simulate turbine rotation speed to predict wind energy production 10 minutes ahead. Four tools are employed: The fuzzy c-means (FCM) approach of adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), grid partitioning (GP) method of adaptive neuro-fuzzy inference system and subtractive clustering (SC) algorithm of adaptive neuro-fuzzy inference system were used for predictions. These methods use historical data as input for the physical parameters to be estimated and estimate the subsequent value as the output. Thus, using only historical data, future values of the considered parameter can be easily predicted without the need for other physical parameters, such as meteorological data or data regarding the design of the mechanical installation, or without solving complex differential equations containing many unknowns. In the study, wind power and rotor rotational speed data from one wind turbine operating in a wind farm is taken into consideration. Among 34 models, LSTM is shown to perform best in capturing real observed wind turbine parameters. In the estimations of wind output power of wind turbine, it is reported that the rates of evaluation criteria were computed as 136.04 kW MAE, 242.64 kW RMSE, and 0.9711 R. Besides, in the predictions of turbine rotational speed, it is notified that 0.72 rpm MAE, 1.16 rpm RMSE, and 0.9452 R values were computed. On the other hand, among the generated ANFIS models, ANFIS-FCM model yields best accurate results with 138.69 kW MAE, 244.40 kW RMSE and 0.9708 R values in wind power, 0.73 rpm MAE, 1.17 rpm RMSE and 0.9451 R values in turbine rotor rotation.

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.

Optimization of Tuned Mass Damper for Steel–Concrete Hybrid Wind Turbine Tower Using Genetic Algorithm

Steel–concrete hybrid towers (SCHTs) have been adopted as a popular supporting structure for wind turbines with the increasing hub height and rotor diameter. The excessive transverse vibration of the supporting tower adversely compromises the turbine’s operation and leads to structural deteriorations. The tuned mass damper (TMD) has been validated as an efficient device integrated with high-rise structures in vibration suppressions. This study aims to provide a reliable method for determining the key parameters of the TMD to obtain the optimal performance in vibration suppressions. This study begins with a rigorous theoretical analysis to investigate the dynamic responses of the SCHT. The dynamic analysis serves as a basis for developing a dynamics model of the SCHT integrated with a TMD, which provides a programmable method to analyze the effect of the TMD on the dynamic responses of the SCHT. Then, an optimization model based on the genetic algorithm (GA) is established to obtain the optimal parameters of the TMD. Finally, a numerical test comprising 12 loading conditions is conducted to analyze the vibration mitigation effect of the TMD. The validation process also provides discussions about the influence of the wind turbine operation states and the fitness function adopted in the GA on the efficiency of the TMD. Those findings demonstrate the effectiveness and limitations of the TMD for mitigating vibrations of the SCHT. It thereby contributes to the understanding and improvement of vibration control strategies for SCHT structures and can guide future design and optimization efforts.

Numerical Methodologies for the Analysis of Horizontal-Axis Floating Offshore Wind Turbines (F-HAWTs): A State-of-the-Art Review

In recent decades, wind turbine installations have become a popular option to meet the world’s growing demand for energy. Both onshore and offshore wind turbines form pivotal components of the electricity sector. Onshore wind energy is now a mature technology, with significant experience gained by wind farm developers and operators over the last couple of decades. However, as a more recent enterprise, the offshore wind industry still requires significantly more development before the technologies and operations reach maturity. To date, floating platforms at sea have been utilised extensively for the oil and gas industry. While a lot of the expertise and technology is transferable to the floating offshore wind industry, significant development work remains; for example, there is significant work required due to the different device types. Compared to floating oil and gas platforms, floating wind turbine platforms have a higher centre of gravity, which influences their performance and complexity. The successful large-scale development of floating offshore wind farms will require significant expertise and learning from the onshore wind, oil, and gas sectors. There are a wide range of software packages available to predict the operational behaviour of floating offshore wind turbines. In spite of this, it is still extremely difficult to create a fully coupled model of a floating wind turbine that can accurately and comprehensively model the turbine aerodynamics, hydrodynamics, servodynamics, structural dynamics, and mooring dynamics. This paper presents details on various fully coupled and uncoupled software packages and methodologies utilised to simulate floating offshore wind turbine performances. Various kinds of mooring systems, floating wind turbines, analysis methods, and experimental validation methods are comprehensively described. This paper serves as a reliable methodological guideline for researchers and wind industry professionals engaged in the design/analysis of wind farm projects.

A Passive Flow Control Technique of a Small-Scale HAWT and TED Analysis Under Yaw Condition Based on Airfoil Concavity

To mitigate the energy loss caused by flow separation of a 300 W small wind turbine, a passive flow control technique based on the airfoil concavity was proposed. The suction surface of the blade was modified with eight different types of concavity, the results showed that the b1 elliptical concavity, with B-spline curves front-and-rear transition, significantly affected the airflow of the airfoil’s suction surface, improving the wind turbine’s aerodynamic performance by 3.26% at maximum. Then, the flow field characteristics of b1, c1, and c4 concave airfoils with typical geometric features under axial flow conditions demonstrated that the b1 airfoil concavity had the greatest impact on flow separation. Moreover, yaw angle was induced, and the wind turbine’s turbulent kinetic energy (TKE) and turbulent energy dissipation (TED) were investigated from the aspects of energy loss. The variation rule of the TED difference between the concave bottom and edge with yaw angle was summarized into an equation that quantitatively explained why the 10° yaw angle was the turning point of the power output, as well as the potential mechanism of concave airfoil-induced power enhancement. These findings provide a foundation for enhancing the aerodynamic performance of large megawatt-class wind turbines.

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 1979Aerokurier1254). The VAWT considered has three NACA0018 straight blades at the Reynolds number of80000based 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.

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.

Study of the Influence of Buildings on the Performance of Micro Wind Turbines in Urban Environments through Computational Simulations

In urban areas, where space is limited, micro wind turbines can be installed on rooftops or structures near buildings. Studying the flow around cylinders near a flat wall is important for understanding how the presence of buildings affects the performance of these micro wind turbines and how they can be optimized to operate in urban environments. This study employs the Pseudospectral Fourier method coupled with the Immersed Boundary method in simulations involving flow around a circular-based cylinder near a wall. This work presents simulations that demonstrate the influence of local Reynolds number with the introduction of a wall, as well as the influence of varying the distance between the cylinder and the wall, referred to as "GAP," on the formation of the vortex wake. The results are compared with other works: experimental and numerical.

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.

A Surrogate-Based Approach for Bend-Twist Coupling Optimization of a Horizontal Axis Wind Turbine Composite Blade

A design study on the effect of bend-twist coupling in a composite blade of horizontal axis wind turbine (HAWT) is conducted. Using specific lamination sequences with unidirectional plies, a laminated composite is obtained whose mechanical stresses are coupled in its bending and twisting. Given the aerodynamic forces on the blade during its operation, mechanical stresses emerge in the material, which, due to its bend-twist coupled behavior, conforms elastically, adapting its geometry to the wind flow. The tailoring of aeroelastic effects can be applied to wind turbine blades to improve the performance of wind turbines. Furthermore, the manufacturing costs, either from material or process perspectives, are potentially reduced with well-designed layup sequences. A blade is first designed according to specific parameters using the Blade Element Momentum Theory (BEMT) under the assumption of total rigidity. These include operational and constructive parameters (e.g., desired output power, airfoil profile characteristics, nominal wind velocity, number of blades, etc.). The effect of bend-twist coupling in the laminated composite is analyzed by fluid-structure interaction (FSI). The layup sequence is parametrized within ANSYS Composites PrepPost (ACP) with different laminations for upper and lower face of the blade, given both geometry and stress distributions differ for each face. The CFD and the structural behavior are coupled in a two-way FSI system, along with material properties evaluated from ACP. Thus, deformations are computed using the pressure distribution from CFD and material properties from ACP. The power coefficient is calculated once the FSI simulation has converged, yielding the resultant torque and rotation, which are then used to evaluate the extracted power.. An optimization routine is implemented to maximize the power extraction as a function of the lamination sequence for the upper and lower faces of the blade. The computational cost of the optimization procedure is reduced by employing radial basis function neural network, acting as a surrogate model. It is predicted an increase of up to 10% in the power coefficient for the generator. The final proposed laminated composite for upper and lower faces of the blade extends the power curve of the generator, allowing it to sustain operation even in overload conditions, delaying stall of the blades and avoiding tower collapse by excessive blade tip axial deflection.

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.

Troubleshooting of wind turbine gearboxes

Abstract Qinghai Province is rich in wind power resources, actively building wind power generation clean energy highland, large installed capacity, and in the rapid expansion, how to achieve the safe and stable operation of the wind power base is particularly critical. Wind turbine gearboxes operate 24 hours a day under poor working conditions, and key components are prone to failure, accounting for the highest proportion of maintenance costs, so it is of great significance to research intelligent assessment methods and key technologies for the health status of wind turbine gearboxes. A dynamic model of wind-machine-network coupling in wind turbine transmission chain is established to investigate the failure mechanism and dynamic evolution of transmission chain gearboxes under variable wind load. A time-frequency energy concentration algorithm based on a synchronous extraction operator is found to explore the mapping relationship between fault sources and signs. Using a multi-source signal fusion strategy and migration learning method for order spectral analysis, a system fault characteristic parameter identification model is established to implement online monitoring and fault prediction of the wind turbine operation condition, and to improve its operation reliability.

Experimental investigation of ice resistance in jacket foundations for large offshore wind turbines

ABSTRACT Each winter, the Bohai Sea in China freezes, forming a thick ice layer that significantly affects the safety of ship and offshore structures. Offshore wind turbines, particularly high-rise structures, are especially vulnerable to collapse under the pressure of sea ice. This paper presents an experimental study on the ice resistance of the steel jacket foundation of a 10 MW offshore wind turbine. The study examines the failure modes, hysteresis curves, stiffness, and energy dissipation capacity of the jacket foundation under ice loading. Results indicate that the branch tube joints within the sea ice zone are prone to fracture, leading to a reduction in the overall stiffness and load-bearing capacity of the foundation. These findings offer a valuable experimental basis for improving the ice resistance performance of offshore wind turbines and enhancing their structural safety in ice-prone regions.