Presence Of Wind Turbines Research Articles

An Evaluation of Inflow Profiles for CFD Modeling of Neutral ABL and Turbulent Airflow over a Hill Model

The implementation of the wind turbine is a major issue in the wind engineering sector. However, the presence of wind turbines in the lower part of the atmospheric boundary layer (ABL) requires an appropriate study for the simulation of turbulent airflow in the wind farm situated on hilly terrain. The use of precise Computational Fluid Dynamics (CFD) simulations for the ABL flow is vital for numerous applications, such as wind energy, building, urban planning, etc. To achieve accurate results, it is imperative that the inlet boundary conditions produce vertical profiles that keep a uniform horizontal distribution (with no streamwise gradients) in the upstream region of the computational domain for all important parameters. A development approach is proposed herein, focused on the imposition of two different inlet profiles when used in combination with the rough z0-type scalable wall function. The horizontal homogeneity of these profiles has been verified by 2D Reynolds averaged Navier-Stokes (RANS) through the examination of a neutral ABL in an empty computational domain using the k-ε turbulence model. The findings indicate that the use of this modeling approach can yield relatively consistent homogeneity of neutral ABL for both inlet boundary conditions. Subsequently, sensitivity analyses were performed on the inflow profiles to forecast the evolution of the bottom half of an idealized truly-neutral ABL and to accurately capture the complex dynamics of atmospheric flows over hilly terrain. This study compares the results with the CRIACIV (Inter-University Research Centre on Building Aerodynamics and Wind Engineering) boundary layer wind tunnel experimental data, showing that the inflow profiles and the presence of topographic complex have a significant impact on air velocity, turbulent kinetic energy and turbulence intensity in the x-direction. The results obtained are in good correlation with published experimental data in the presence of the hill surface.

Comparison of Different Standard Test Methods for Evaluating Greases for Rolling Bearings under Vibration Load or at Small Oscillation Angles

Rolling bearings operated at small oscillation angles or exposed to vibrations during standstill show typical damage after only a short period of operation. This can be false brinelling damage, so-called standstill marks or classic fretting damage (fretting corrosion, tribo-oxidation). It is important to differentiate here according to the amplitude-ratio x/2b, which indicates the ratio between the rolling element Motion (x) and the Hertzian contact half-axis (b). Depending on this ratio, suitable laboratory test methods must be used to test the lubricating grease practically for the particular application. For this purpose, the Fafnir wear test, according to the standard of the American Society for Testing and Materials ASTM D4170, is also listed in the current high-performance multi-use specification of the National Lubricating Grease Institute (NLGI) as a release test for lubricating greases. In Europe, the SNR-FEB2 test is frequently used, which is also required to release greases in the blade bearings of wind turbines, among other things. In the case of standstill marks due to very small oscillation angles or vibrations, the Mannheim Tribology Competence Center (KTM) has developed a special test now established in the industry. The oscillating angles vary in these three different standard tests in the range from ±6° in the Fafnir test to ±3° in the SNR-FEB2 test to ±0.5° in the KTM standstill marking test; the x-to-2b ratios range from 5.5 (Fafnir) to 3.4 (SNR) to 0.5 (KTM). This paper will explain the scientific basis for these special operating and test conditions and compare test results of specially prepared model greases in these three standard rolling bearing tests, two test variations and a classical fretting test under oscillating sliding friction (ASTM D7594). The paper’s main objective is to show that the suitability of grease for such an application depends strongly on the prevailing operating conditions. Different tests in this field are, therefore, not interchangeable. Good results in one test do not automatically mean good results in a similar test at first glance. Therefore, selecting the right test for the application is important.

A study on the three key concepts of White Etching Crack failure mode in its very early stages - Contrast with different testing methods

Despite the great attention that WECs have received from researchers over the last few years, the earliest stages of this failure are still under debate. The wide variety of drivers studied, and the different methodologies employed to reproduce this failure mode, raise the question; do different methodologies lead to different initiation mechanisms? The present work aims to understand the most premature stages of WEC formation in 100Cr6 steel discs tested under RCF conditions and pre-charged with hydrogen. Firstly, three key aspects in the WEC formation process were identified, which have been extensively studied by other authors, but at intermediate or late stages of formation. These aspects are, (1) microstructural alteration formation first steps, (2) newly formed grain growth and (3) carbide dissolution. Secondly, samples representative of early stages were analysed by SEM/EDX/EBSD. The results of this research suggest that the early stages of these aspects are in agreement with the intermediate and late stages presented by other authors in the case of both WEC critical oil lubricated bearings and failed field bearings. Therefore, tribometer tests preceded by hydrogen uptake seem to recreate the tribological conditions found in wind turbine bearings. In addition, a very detailed failure initiation mechanism is proposed in this work.

Method for Evaluating the Reliability and Competitive Failure of Wind Turbine Gearbox Bearings Considering the Fault Incubation Point

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines.

Stochastic bi-level allocation of electric vehicle charging stations in the presence of wind turbines, crypto-currency loads and demand side management

Stochastic bi-level allocation of electric vehicle charging stations in the presence of wind turbines, crypto-currency loads and demand side management

Reliability Sensitivity Analysis of Main Shaft Bearings of Wind Turbines Subject to Subsurface Stress

As the core component of a wind turbine, the performance of main shaft bearings directly affects the transmission efficiency and reliability of wind turbines. To the best of our knowledge, few reliability analyses of wind power bearings have been carried out with the consideration of mechanical properties. In this paper, a finite element model is established to calculate the subsurface stress of the main shaft bearing of a wind turbine, considering the structural thicknesses, the friction conditions, and the interference conditions of the bearing. The randomness of several factors is considered, including structural thicknesses, material parameters, friction coefficients and the interference of the bearing. Latin hypercube design is used to get sample points, and the bearing’s mechanical responses of these sample points are analyzed. Through the data of these sample points, a Kriging model is established. The comparison with the finite element results shows that the Kriging model greatly improves the computational efficiency of the finite element model, with a relative error result of only 3.80 × 10−5. The Monte Carlo simulation method is applied to analyze the reliability and sensitivity of the bearing’s subsurface stress. The results show that an increase in the inner ring thickness will improve the bearing’s stress reliability, while an increase in other parameters will reduce the bearing’s stress reliability, including outer ring thickness, roller length, material elastic modulus, density, bearing and stationary shaft interference, and friction coefficients. The research results provide a reasonable reference for optimizing the design of the structure, assembly and material selection for main shaft bearings of wind turbines.

A novel method of wind turbine bearing fault diagnosis based on gradient projection sparse reconstruction and fractional Fourier transform and improved convolutional long short-term memory

The bearing is one of the most critical components of a wind turbine, and its condition determines whether the wind turbine can work normally or not. And in recent years, the increasing number of wind turbines leads to more and more data collected for bearing condition monitoring, which brings a great challenge to data transmission and preservation. In addition, the vibration signal is one-dimensional, and directly using one-dimensional signals can avoid the complex calculations caused by increasing the dimensionality of the data. In this article, a new fault diagnosis method for wind turbine bearings is proposed using one-dimensional data as input. The method is based on sparsely reconstructed gradient projection and fractional Fourier transform (GPSR-FRFT) for feature extraction of the signal, and a modified convolutional long short-term memory (CNN-LSTM) is used for fault classification of the data. Firstly, the signal is sparsely expressed by the GPSR algorithm, and the original signal is compressed and observed using compressed sensing so that the observed signal can be transmitted instead of the original signal, this process can reduce data samples to save storage space. The original signal is recovered by the inverse solution of compressed sensing at the terminal, and this step can reduce noise interference. Secondly, the time-frequency characteristics of the signal are obtained using the FRFT transform. In addition, in order to solve the problem of insufficient learning ability and low accuracy of convolutional long short-term memory (CNN-LSTM), an improvement was made by adding a jump connection layer to the convolutional layer to improve the training speed and learning ability. The resulting signal is input to an improved CNN-LSTM network, and experimental analysis proves the superiority of the proposed method.

A Comparative Review of the Lubrication of Bearings in Wind Turbines and Spacecraft to Address Wind Turbine Bearing Failures

A Comparative Review of the Lubrication of Bearings in Wind Turbines and Spacecraft to Address Wind Turbine Bearing Failures

Lesser prairie‐chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape

The overlap of renewable wind energy with the range of lesser prairie‐chickens Tympanuchus pallidicinctus raises concern of population declines and habitat loss. Lesser prairie‐chickens are adversely affected by landscape change; however, it is unclear how this species may respond to wind energy development. Therefore, managers and wind energy developers are currently tasked with making management or siting recommendations of future wind energy facilities based on lesser prairie‐chicken behavioral responses to other forms of anthropogenic development or responses of other grouse species to wind energy development. The current strategy of siting wind turbines in cultivated cropland within lesser prairie‐chicken range has not been evaluated for its effectiveness at minimizing potential adverse impacts. We captured 60 female and 66 male lesser prairie‐chickens from leks located along a gradient from wind turbines in southern Kansas, USA from 2017–2021. Over the study period, we collected lesser prairie‐chicken location data and demographic information to evaluate resource selection, movement, and demography relative to environmental predictors and metrics associated with the wind energy facility. Lesser prairie‐chickens used habitats in close proximity to wind turbines, provided that turbine density was low; however, avoidance associated with cultivated cropland appeared to be more predictive than the presence of wind turbines. We observed movement between turbines suggesting that wind turbines did not act as a barrier to local movements. We did not detect an influence of wind turbines on nest success or individual survival during breeding or non‐breeding periods, a relationship that is consistent among multiple grouse species using habitats near wind energy infrastructure. Additional research is necessary to evaluate impacts associated with wind energy development in intact lesser prairie‐chicken habitats, but placing wind turbines in cultivated croplands or other fragmented landscapes appears to be an important siting measure when considering wind energy facility siting across the lesser prairie‐chicken range.

Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines

Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines

Abnormality Detection Method for Wind Turbine Bearings Based on CNN-LSTM

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find.

Experimental Investigations on Wear in Oscillating Grease-Lubricated Rolling Element Bearings of Different Size and Type

Grease-lubricated rolling element bearings can suffer from wear due to lubricant starvation under certain oscillating operating conditions. Especially for large-scale slewing bearings, such as blade bearings in wind turbines, experimental investigations are complex compared to small-scale reference testing. For an easier manner of testing, it is desirable to know whether the results of small-scale testing are applicable to larger-sized bearings. In this work, three different bearing types were tested and compared to already published results from a small-scale ACBB with a pitch diameter of 60 mm. The newly tested bearing types comprise a downscaled blade bearing (4-point contact double row ball bearing) with a pitch diameter of 673 mm, a small-scale CRTB with a pitch diameter of 77.5 mm and another ACBB with a pitch diameter of 95 mm. Qualitatively, all tested bearings show similar wear behaviour in terms of friction energy when operation parameters are varied. With higher oscillation frequency, damage becomes more severe. The oscillation amplitude shows three distinctive regimes. Within the range of small amplitudes, an increase in amplitude leads to more pronounced damage. We observe a threshold amplitude where this is no longer the case; a further increase in amplitude counteracts wear initiation until a final threshold is reached, beyond which no more wear is observed. These findings are in accordance with the reference results of the small-scale ACBB. Direct comparison between point and line contact shows that the latter is more prone to wear initiation under grease-lubricated, oscillating operating conditions. Furthermore, a previously introduced empirical number shows good performance in assessing critical operating parameters of the different bearing types. Specifically, harmful operating conditions can be classified for all studied bearing types with an accuracy of 78%. This method can be useful to assess operating conditions of greased, oscillating, rolling element bearings, e.g., to assess different pitch controllers or designs of slewing bearings.

Exploring limiting factors of wear in pitch bearings of wind turbines with real-scale tests

Abstract. Oscillating movements under load can cause wear in rolling bearings. Blade bearings of wind turbines are subject to both. To know how to avoid wear in these bearings is important since they ensure the operational safety of the turbine. Oscillations of blade bearings vary in load, speed, and amplitude. The objective of this work is to find limits of these operating parameters with regards to wear occurrence. To this end several tests with real-size bearings were carried out. The test parameters are based on typical operating conditions of a reference turbine. The size of the bearings and the test parameters differ from other published tests for oscillating bearings, where often scaled bearings are used results. The test results show that wear occurs for every tested combination of load, speed, and amplitude of a steady oscillating movement. Even if the wear characteristics differ between tests, each of them resulted in wear. Hence, no wear limits can be defined with the tested grease and within typical operating conditions of a wind turbine below which wear does not occur. Tests with a discontinuity in the steady oscillation movement, however, did not result in wear. Such discontinuities can be longer movements embedded in steady oscillations. They are characteristic to wind turbine operation, where longer movements are a reaction to wind gusts.

Simulation methodology for the identification of critical operating conditions of planetary journal bearings in wind turbines

The usage of journal bearings as planetary bearings in wind turbines instead of roller bearings has become more common in recent years. Their usage is advantageous, due to smaller installation space needed compared to roller bearings allowing for higher power densities of wind turbine drive trains. However, this technology presents a challenge since there is currently no standardized approach for the design of planetary journal bearings regarding wear. Due to varying wind speeds and dynamic operating events a large variation of loads has to be considered in the design process of a planetary journal bearing for wind turbines. Some of these loads are considered potentially critical to the journal bearing in terms of wear. Identifying these critical load areas early in the design phase supports a reliable bearing design and wind turbine operation.This paper introduces a method to identify critical operating conditions for planetary journal bearings using a simulation tool chain, which couples a multi body simulation (MBS) model of a wind turbine with an elasto-hydrodynamic (EHD) model of the planetary journal bearing. Based on the EHD results critical operating conditions are determined for the planetary bearing. Furthermore, methods are implemented to reduce the number of required EHD simulations for analysing the bearing design. The combination of the identification of critical operating conditions, while reducing the computational effort leads to a simulation methodology, which enables a faster bearing design assessment considering the wide variation of wind turbine operating conditions. The applicability of this method is demonstrated by a simplified use case.Firstly, this paper introduces the MBS model and the parameter space that describes possible combinations of bearing loads such as forces, moments and rotational speed. Due to the number of combinations and the EHD computing effort, the identified parameter space is secondly sampled statistically to reduce the simulation effort. A risk map is derived from the EHD results, to easily indicate potentially critical operating conditions for the planetary journal bearing.

A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings

The accurate probabilistic prediction of remaining useful life (RUL) of bearings plays an important role in ensuring the safe operation of wind turbine maintenance decision making. However, it is still a challenge to improve prediction accuracy and quantify prediction uncertainty, leading to the inability of precision and reliable prediction results. Accordingly, this paper proposed a novel probabilistic RUL prediction method combined the parallel GRUs with dual-stage attention mechanism (PDAGRU) prediction model with the non-parametric uncertainty quantification approach to overcome the limitations. In the PDAGRU model, the dual-stage attention mechanism is developed to improve the capability of degradation information extraction. Meanwhile, the parallel structure can enhance prediction accuracy and help quantify model uncertainty. The proposed uncertainty quantification approach with less prior knowledge can provide probabilistic RUL prediction results based on the kernel density estimation (KDE) and Monte Carlo (MC) dropout. Moreover, a first prediction time (FPT) determination method based on the isotonic regression is developed to more accurately reflect the degradation trajectory of bearings’ RUL. Two cases including simulated data and real-world data are deployed to verify the effectiveness of the proposed method. Compared with other methods, the superiority of the proposed method is verified. The RUL prediction accuracy and interval coverage probability for wind turbine bearings are high to 90.4% and 99.7% respectively.

A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis

Bearings are crucial components that decide whether or not a wind turbine can work smoothly and that have a significant impact on the transmission efficiency and stability of the entire wind turbine’s life. However, wind power equipment operates in complex environments and under complex working conditions over long time periods. Thus, it is extremely prone to bearing wear failures, and this can cause the whole generator set to fail to work smoothly. This paper takes wind turbine bearings as the research object and provides an overview and analysis for realizing fault warnings, avoiding bearing failure, and prolonging bearing life. Firstly, a study of the typical failure modes of wind turbine bearings was conducted to provide a comprehensive overview of the tribological problems and the effects of the bearings. Secondly, the failure characteristics and diagnosis procedure for wind power bearings were examined, as well as the mechanism and procedure for failure diagnosis being explored. Finally, we summarize the application of fault diagnosis methods based on spectrum analysis, wavelet analysis, and artificial intelligence in wind turbine bearing fault diagnosis. In addition, the directions and challenges of wind turbine bearing failure analysis and fault diagnosis research are discussed.

Fault Diagnosis of Wind Turbine Bearings Based on CEEMDAN-GWO-KELM

To solve the problem of fault signals of wind turbine bearings being weak, not easy to extract, and difficult to identify, this paper proposes a fault diagnosis method for fan bearings based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Algorithm Optimization Kernel Extreme Learning Machine (GWO-KELM). First, eliminating the interference of noise on the collected vibration signal should be conducted, in which the wavelet threshold denoising approach is used in order to reduce the noise interference with the vibration signal. Next, CEEMDAN is used to decompose the signal after a denoising operation to obtain the multi-group intrinsic mode function (IMF), and the feature vector is selected by combining the correlation coefficients to eliminate the spurious feature components. Finally, the fuzzy entropy for the chosen IMF component is input into the GWO-KELM model as a feature vector for defect detection. After diagnosing the Case Western Reserve University (CWRU) dataset by the method presented in this research, it is found that the method can identify 99.42% of the various bearing states. When compared to existing combination approaches, the proposed method is shown to be more efficient for diagnosing wind turbine bearing faults.

Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds

Recent years have seen the rapid development and marvelous achievement of deep learning-based fault diagnosis (FD) methods which assume that training data and testing data have the same distribution. However, in real FD of wind turbine bearing (WTB), the particularity of time-varying speeds makes a huge difference in the distribution of training data and testing data, greatly increasing the difficulty of FD. Accordingly, in this paper, a novel deep residual deformable subdomain adaptation framework is proposed for cross-domain failure diagnosis of WTB under time-varying speeds. In the proposed approach, the traditional residual network is improved by using a deformable convolution module to replace plain counterparts, which can make the feature representation of an object adapt its configuration and enhance the ability of the model to extract transferable features. Moreover, the popular FD model based on domain adversarial neural nets and global maximum mean discrepancy is improved by removing the adversarial training mechanism and employing a local maximum mean discrepancy to align the distributions of the identical fault type in different domains, making the diagnostic model simpler and more efficient. Two experimental cases under time-varying speeds are conducted to analyze the performance of the proposed approach and the results indicate that this method can utilize the knowledge in the source domain to diagnose the fault in the target domain. Compared with the existing methods, the diagnosis accuracy and efficiency are significantly improved, demonstrating its effectiveness and potential applications in fault transfer diagnosis of wind turbine bearing.

A chance-constrained optimization framework for transmission congestion management and frequency regulation in the presence of wind farms and energy storage systems

• Providing a chance-constrained framework for congestion management and frequency regulation. • Evaluating the performance of the model in both normal and emergency conditions. • Investigating the effect of spinning reserve rate on congestion, LMP and frequency. • Investigating the effect of EES systems on congestion, LMP and frequency. This paper presents a chance-constrained optimization framework for transmission congestion management in the presence of wind turbines and electrical energy storage (EES) systems. In the proposed model, the uncertainties of the wind farms’ output power and electricity demand are considered, and in order to model these uncertainties, the chance-constrained method is used. In order to reduce the computational burden in this problem, a filtering algorithm is proposed that detects sensitive lines and applies security constraints only on them. The results illustrate that applying the proposed algorithm has reduced the solution time by about 83%. In addition, the proposed model is implemented on a modified IEEE 30-bus test system and solved under normal and emergency conditions (N-1). The simulation results demonstrate that considering the chance constraints, by increasing the spinning reserve, leads to a decrease in the amount of energy not supplied (ENS) and consequently operating costs during the contingent events. The numerical results illustrate that considering the spinning reserves, despite a 10% increase in operating costs under normal operating conditions, has reduced the amount of ENS by about 18% during the outage of the thermal unit. In addition, the results demonstrate that EES systems keep the system frequency within the allowable range under emergency conditions due to their fast ramp rate.

Early Fault Detection in the Main Bearing of Wind Turbines Based on Gated Recurrent Unit (GRU) Neural Networks and SCADA Data

Failures in the main bearings of wind turbines are critical in terms of downtime and replacement cost. Early diagnosis of their faults would lower the levelized cost of wind energy. Thus, this work discusses a gated recurrent unit (GRU) neural network, which detects faults in the main bearing some months ahead (when the event that initiates/develops the failure releases heat) the actual fatal fault materializes. GRUs feature internal gates that govern information flow and are utilized in this study for their capacity to understand whether data in a time series is crucial enough to preserve or forget. It is noteworthy that the proposed methodology only requires healthy supervisory control and data acquisition (SCADA) data. Thus, it can be deployed to old wind parks (nearing the end of their lifespan) where specific high-frequency condition monitoring sensors are not installed and to new wind parks where faulty historical data do not exist yet. The strategy is trained, validated, and finally tested using SCADA data from an in-production wind park composed of nine wind turbines.