Key components of the horizontal axis wind turbine

NREL is the only organization in the US that is accredited to conduct tests of wind turbine systems and components in accordance with International Electrotechnical Commission (IEC) test standards. The National Wind Technology Center houses the only full-scale wind turbine blade drivetrain test facilities in the US. To maintain these facilities, accreditation, and the acceptance of the wind industry, NREL must strive continuously to maintain and improve staff capabilities, procedures, facility and equipment. In addition, as wind turbines continue to increase in size, NREL must improve its capability to test the larger components associated with these new turbines. NREL is currently investigating methods to increase blade and drivetrain testing capabilities. Mitsubishi Power Systems (MPS) must also strive to meet the demands of the evolving wind energy industry by developing, proving and manufacturing new, cost-effective blades for its turbines. Mitsubishi has established a joint venture with TPI composites of Rhode Island for manufacturing blades to be installed on US wind turbines. There is an obvious advantage to test these prototype blades in a US facility. The NREL blade test facility is uniquely capable of fulfilling this need. In addition, NREL can provide other testing support to MPS through the use of its dynamometer facility for drivetrain testing and through the use of NREL’s field-testing capabilities for testing wind turbine systems. This CRADA provides the opportunity for both organizations to achieve critical goals in development of wind energy in the US. MPS obtains verification of its wind turbine components and system. NREL obtains improved test capabilities.

PurposeThe purpose of this paper is to analyze wind characteristics and their effects on wind turbine components and energy generation at the candidate site.Design/methodology/approachThe methodology covered the detailed investigation of wind characteristics using Weibull k and c parameters and standard deviation at 30 m above the ground level (AGL). The wind shear coefficient and air density were also studied. The weight model was developed to determine the effects on wind turbine components and energy generation. At last, an economic assessment was carried out to determine the pre- and post-effects of the weight model on the cost of energy per kilowatt-hour.FindingsThe mean standard deviation, Weibull k parameter and Weibull c parameter were found to be 2.157, 2.617 and 6.087 m/s, respectively, at 30 m for a period of a year. The mean wind shear coefficient was found to be 0.176 for a year. The calculated results showed that site-specific midrange and amplitude force were 40.95 per cent and 37.75 per cent on wind turbine mechanical components, respectively. The average rise in force and drop in energy was found to be 35.50 per cent and 47.55 per cent, respectively. The lift coefficient, drag coefficient and pitching moment considering values (a, 0.1 and 0.2) showed an increase in force on wind turbine components that resulted in a drop in energy. The cost assessment results showed that the cost of energy was increased from US$0.032/kWh to 0.0466/kWh for wind turbine A.Practical implicationsAn accurate determination of the weight factor is necessary for near-reality assessment of wind energy yield and rise of force on the wind turbine. The results paved the way for site-specific design optimization of wind turbines.Originality/valueThe study contributes to the site-specific wind characteristic-based weight model to determine the effects of wind loads on wind turbine components and energy generation and compared with the specified design standard. The lift coefficient, drag coefficient and pitching moment coefficient show a rise in the force while considering the weight factor values. The results show that the site has the potential to generate energy at the lowest cost per kilowatt-hour, but it needs wind turbine design adjustments according to site-specific wind characteristics. If site-specific wind characteristics are considered, it would lead to maximum energy generation and high reliability of wind turbine components.

Owing to its being an important component of renewable energy, wind energy is a kind of power generation method with the most mature, highly developed technologies and broad commercial prospects. The stable and efficient conversion of wind energy by wind turbines depends not only on the reliability of the wind power generation equipment itself, but also on the wind turbine control system, which, in turn, contributes to long-term safe and reliable operation of the wind farm fleet. It is exactly the wind turbine control system that is the main subject of this study. The key to efficient and stable operation of the entire wind energy conversion system is the control technology, which includes yaw control, pitch angle control, and maximum power point tracking control. The active yaw control system is one of the important components of a horizontal axis wind turbine's control system. To eliminate the uncertainty of wind direction influence on the turbine power output, a composite yaw control system has been checked. By using an active yaw system and maximum power point tracking system, the turbine position and its rotation speed are adjusted to enable the wind turbine to accurately track the wind direction and capture the wind energy to the fullest extent.

Gear box has the high failure rate among the components in wind turbines and the vibration signal analysis is the most common technology in its fault diagnosis. Gearbox vibration signals of wind turbines have complicated compositions, strong backgrou

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

We have successfully designed, developed, and tested the blades, a wind lens, and a wind deflection ramp. The lens and ramp were capable of increasing the output power of the wind turbine. Since the power output of an ideal wind turbine is a function of wind speed cubed, these modifications were designed to increase the velocity of the wind before it reached the blades. The lens creates a low-pressure region at the exit area of the lens, which generates vortices and draws the wind through the blades at a higher velocity. The wind deflection ramp, which was designed to operate at a wind speed of 7 m/s, increased the power output by directing more wind into the turbines blades. The test data was compared to theoretical calculations to evaluate the results. All of the design and testing methodology as well as theoretical and experimental results are explained throughout the body of this paper.

Expert System of Fault Diagnosis for Gear Box in Wind Turbine

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Failure Prognostic Schemes and Database Design of a Software Tool for Efficient Management of Wind Turbine Maintenance

Against the background of the EU Climate Law of 2021, the generation of wind energy plays a special role. A brief description of how a wind turbine works is followed by a classification (Onshore and Offshore turbines) with advantages and disadvantages. The components of wind turbines are then discussed, with a particular focus on the foundation, towers and blades and challenges during installation and operation are explained. With artificial intelligence, it seems possible to improve the accuracy of condition assessment of wind power plants, optimize resource allocation and minimize downtime.

Torsional vibrations present in the drivetrain excited by turbulent winds or grid disturbances (often manifesting as voltage sags) can produce severe stresses on the components of wind turbines. This study presents a damping method for the rotor side converter of a doubly fed induction generator (DFIG)-based wind turbine to improve system damping during small grid disturbances. Within the damping strategy, torsional damper is included into the power loop of the rotor side converter to provide damping for the system. During vibrations, a well-designed low-pass filter (LPF) is employed by the torsional damper to extract the dynamics of the generator speed and to generate a damping signal. To demonstrate the proposed damping approach, the small-signal stability model of a wind turbine connected to an infinite bus system is presented, and the eigenvalue analysis is conducted to verify the damping effect introduced by the torsional damper. In the case study, grid voltage sag at the point of common coupling (PCC) is applied to excite the torsional vibration of the wind turbine shaft. The validity and effectiveness of the proposed damping approach are testified by using the simulation results against theory analysis.

Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under arbitrary load and speed variations in a laboratory setup of a pitch drive.

The gearbox is one of the crucial components in wind turbines, and its performance degradation would give rise to out-of-order or even damage. To accurately identify the fault of the gearbox, a novel fault diagnosis approach based on the ensemble model and the dung beetle optimization algorithm is introduced. Firstly, an ensemble learning model with different activation functions is established. Secondly, the dung beetle optimization is applied to select the base models for improving the performance and generalization ability of the ensemble model. Finally, the proposed method is tested with a gearbox dataset. The experimental results show that the proposed approach achieves higher diagnostic accuracy on the gearbox dataset.

Wind farms use several wind turbines to generate electricity and provide a renewable source of energy. However, due to large forces as a result of hourly and seasonal variations in wind speed and direction, wind turbines experience stochastic loading that often lead to failures of wind turbine components such as the gearbox and generator. Wind turbine failures result in costly repairs and loss in revenue, while stochastic loading makes it difficult to predict the actual condition of a wind turbine. Consequently, it is challenging to schedule maintenance actions to avoid wind turbine component failures. In this paper, we present a stochastic discrete event system specification (STDEVS) model of a wind turbine component. In particular, we consider the wind turbine gearbox, which is one of the components that is often prone to failure, and model its stochastic degradation process. We implement a wind farm simulation with the STDEVS gearbox model and report on the impact of gearbox failures and maintenance scheduling on the performance of a wind farm over a 20-year period.