scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farms ◽  
Operating Conditions ◽  
Vibration Data ◽  
Bearing Diagnostics ◽  
Scattering Transform ◽  
The One ◽  
Envelope Spectrum ◽  
High Effort

Abstract As a renewable, unlimited and free resource, wind energy has been intensively deployed in the past to generate electricity. However, the maintenance of Wind Turbines (WTs) can be challengeable. On the one hand, most wind farms operate in remote areas and on the other hand, the dimension of WTs’ tip/hub/rotor are usually enormous. In order to prevent abrupt breakdowns of WTs, a number of Condition Monitoring (CM) methods have been proposed. Focusing on bearing diagnostics, Squared Envelope Spectrum is one of the most common techniques. Moreover in order to identify the optimum demodulation frequency band, fast Kurtogram, Infogram and Sparsogram are nowadays popular tools evaluating respectively the Kurtosis, the Negentropy and the Sparsity. The analysis of WTs usually requires high effort due to the complexity of the drivetrain and the varying operating conditions and therefore there is still need for research on effective and reliable CM techniques for WT monitoring. Thus the purpose of this paper is to investigate a blind and effective CM approach based on the Scattering Transform. Through the comparison with state of the art techniques, the proposed methodology is found more powerful to detect a fault on six validated WT datasets.

scholarly journals Prognostics-based adaptive control strategy for lifetime control of wind turbines

2021 ◽  
Author(s):  
Edwin Kipchirchir ◽  
Manh Hung Do ◽  
Jackson Githu Njiri ◽  
Dirk Söffker
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Control Strategy ◽  
Fatigue Loading ◽  
Operational Reliability ◽  
Operating Conditions ◽  
Rotor Blades ◽  
Time Step ◽  
Damage Level ◽  
Structural Loading

Abstract. Variability of wind profiles in both space and time is responsible for fatigue loading in wind turbine components. Advanced control methods for mitigating structural loading in these components have been proposed in previous works. These also incorporate other objectives like speed and power regulation for above-rated wind speed operation. In recent years, lifetime control and extension strategies have been proposed to guaranty power supply and operational reliability of wind turbines. These control strategies typically rely on a fatigue load evaluation criteria to determine the consumed lifetime of these components, subsequently varying the control set-point to guaranty a desired lifetime of the components. Most of these methods focus on controlling the lifetime of specific structural components of a wind turbine, typically the rotor blade or tower. Additionally, controllers are often designed to be valid about specific operating points, hence exhibit deteriorating performance in varying operating conditions. Therefore, they are not able to guaranty a desired lifetime in varying wind conditions. In this paper an adaptive lifetime control strategy is proposed for controlled ageing of rotor blades to guaranty a desired lifetime, while considering damage accumulation level in the tower. The method relies on an online structural health monitoring system to vary the lifetime controller gains based on a State of Health (SoH) measure by considering the desired lifetime at every time-step. For demonstration, a 1.5 MW National Renewable Energy Laboratory (NREL) reference wind turbine is used. The proposed adaptive lifetime controller regulates structural loading in the rotor blades to guaranty a predefined damage level at the desired lifetime without sacrificing on the speed regulation performance of the wind turbine. Additionally, significant reduction in the tower fatigue damage is observed.

scholarly journals Modeling and simulation of forces applied to the horizontal axis wind turbine rotors by the vortex method coupled with the method of the blade element

2021 ◽  
Vol 12 (1) ◽  
pp. 413
Author(s):  
Ibtissem Barkat ◽  
Abdelouahab Benretem ◽  
Fawaz Massouh ◽  
Issam Meghlaoui ◽  
Ahlem Chebel
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Vortex Method ◽  
Operating Conditions ◽  
Horizontal Axis ◽  
Rotor Blades ◽  
Good Representation ◽  
Horizontal Axis Wind Turbine ◽  
Blade Element

This article aims to study the forces applied to the rotors of horizontal axis wind turbines. The aerodynamics of a turbine are controlled by the flow around the rotor, or estimate of air charges on the rotor blades under various operating conditions and their relation to the structural dynamics of the rotor are critical for design. One of the major challenges in wind turbine aerodynamics is to predict the forces on the blade as various methods, including blade element moment theory (BEM), the approach that is naturally adapted to the simulation of the aerodynamics of wind turbines and the dynamic and models (CFD) that describes with fidelity the flow around the rotor. In our article we proposed a modeling method and a simulation of the forces applied to the horizontal axis wind rotors turbines using the application of the blade elements method to model the rotor and the vortex method of free wake modeling in order to develop a rotor model, which can be used to study wind farms. This model is intended to speed up the calculation, guaranteeing a good representation of the aerodynamic loads exerted by the wind.

Dynamic Load Response Analysis on Yaw Bearing of Wind Turbine to Turbulent Wind

2019 ◽  
Author(s):  
Jianwen Xu
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Gumbel Distribution ◽  
Operating Conditions ◽  
Likelihood Method ◽  
Coupled Analysis ◽  
Response Analysis ◽  
Turbulent Wind ◽  
Load Response ◽  
Yaw Bearing

Abstract Wind turbines are subjected to dynamic loads during their service life. The yaw bearing is an important part which also bears these loads. In this study, a series of 5-megawatt (MW) wind turbines are analyzed for their dynamic response under normal operating conditions while exposed to turbulent wind. These models are Onshore, Monopile, ITI Barge, Spar, Tension-Leg Platform (TLP), Semi-Submerisible. TurbSim is used to prescribe turbulent-wind inflow and a time domain FAST code is applied in order to conduct the Aero-Hydro-Servo-Elastic coupled analysis on the yaw loads of the wind turbines. Three different average wind velocities are examined to compare the load response of the wind turbine to turbulent wind on the yaw bearing. A Gumbel distribution coupled maximum likelihood method is used to predict ultimate loads. And the rain flow counting algorithm, the linear cumulative damage law and S-N curve theory are used to predict the damage equivalent load. The results should aid the fatigue design of yaw bearing and the yaw control system according to different wind turbine design.

scholarly journals The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6348
Author(s):  
Chao Zhang ◽  
Haoran Duan ◽  
Yu Xue ◽  
Biao Zhang ◽  
Bin Fan ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Stochastic Resonance ◽  
Operating Conditions ◽  
Detection Accuracy ◽  
Wind Turbine Gearbox ◽  
Resonance Model ◽  
Function Type ◽  
Vibration Signals ◽  
Fourier Decomposition ◽  
Bearing Fault

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

Experimental Comparison of Three Floating Wind Turbine Concepts

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Andrew J. Goupee ◽  
Bonjun J. Koo ◽  
Richard W. Kimball ◽  
Kostas F. Lambrakos ◽  
Habib J. Dagher
Keyword(s):  
Renewable Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Scale Model ◽  
Experimental Comparison ◽  
System Response ◽  
Advantages And Disadvantages ◽  
Floating Wind Turbine ◽  
Wind Resource

Beyond many of Earth's coasts exists a vast deepwater wind resource that can be tapped to provide substantial amounts of clean, renewable energy. However, much of this resource resides in waters deeper than 60 m where current fixed bottom wind turbine technology is no longer economically viable. As a result, many are looking to floating wind turbines as a means of harnessing this deepwater offshore wind resource. The preferred floating platform technology for this application, however, is currently up for debate. To begin the process of assessing the unique behavior of various platform concepts for floating wind turbines, 1/50th scale model tests in a wind/wave basin were performed at the Maritime Research Institute Netherlands (MARIN) of three floating wind turbine concepts. The Froude scaled tests simulated the response of the 126 m rotor diameter National Renewable Energy Lab (NREL) 5 MW, horizontal axis Reference Wind Turbine attached via a flexible tower in turn to three distinct platforms, these being a tension leg-platform, a spar-buoy, and a semisubmersible. A large number of tests were performed ranging from simple free-decay tests to complex operating conditions with irregular sea states and dynamic winds. The high-quality wind environments, unique to these tests, were realized in the offshore basin via a novel wind machine, which exhibited low swirl and turbulence intensity in the flow field. Recorded data from the floating wind turbine models include rotor torque and position, tower top and base forces and moments, mooring line tensions, six-axis platform motions, and accelerations at key locations on the nacelle, tower, and platform. A comprehensive overview of the test program, including basic system identification results, is covered in previously published works. In this paper, the results of a comprehensive data analysis are presented, which illuminate the unique coupled system behavior of the three floating wind turbines subjected to combined wind and wave environments. The relative performance of each of the three systems is discussed with an emphasis placed on global motions, flexible tower dynamics, and mooring system response. The results demonstrate the unique advantages and disadvantages of each floating wind turbine platform.

scholarly journals Remote surface damage detection on rotor blades of operating wind turbines by means of infrared thermography

2018 ◽  
Author(s):  
Dominik Traphan ◽  
Iván Herráez ◽  
Peter Meinlschmidt ◽  
Friedrich Schlüter ◽  
Joachim Peinke ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Flat Plate ◽  
Infrared Thermography ◽  
Wind Turbines ◽  
Performance Monitoring ◽  
Surface Defects ◽  
Early Stage ◽  
Rotor Blades ◽  
Stereoscopic Particle Image Velocimetry ◽  
Monitoring Method

Abstract. Wind turbines are constantly exposed to wind gusts, dirt particles, and precipitation. Depending on the site, surface defects on rotor blades emerge from the first day of operation on. While erosion increases quickly with time, even small defects can affect the performance of the wind turbine due to nonlinear interaction. Consequently, there is a demand for a remote and easily applicable condition monitoring method for rotor blades that is capable of detecting surface defects at an early stage. In this work it is analyzed if infrared thermography (IRT) can meet these requirements by visualizing differences in the thermal transport and the corresponding surface temperature of the wall-bounded flow. Firstly, a validation of the IRT method against stereoscopic particle image velocimetry measurements is performed comparing both types of experimental results for the boundary layer of a flat plate. Then, the main characteristics of the flow in the wake of generic surface defects on different types of lifting surfaces are studied both experimentally and numerically: temperature gradients behind protruding surface defects on a flat plate and a DU 91-W2-250 profile are studied by means of IRT. The same is done with the wall shear stress from RANS simulations of a wind turbine blade. It is consistently observed both in the experiments and the simulations that turbulent wedges are formed on the flow downstream of generic surface defects. These wedges provide valuable information about the kind of defect that generates them. At last, experimental and numerical performance measures are taken into account for evaluating the aerodynamic impact of surface defects on rotor blades. We conclude that the IRT method is a suitable remote condition and performance monitoring technique for detecting surface defects on wind turbines at an early stage.

Numerical Modeling of the Effects of Leading-Edge Erosion and Trailing-Edge Damage on Wind Turbine Loads and Performance

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Francesco Papi ◽  
Lorenzo Cappugi ◽  
Sebastian Perez-Becker ◽  
Alessandro Bianchini
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Prolonged Exposure ◽  
Leading Edge ◽  
Future Generation ◽  
Operating Conditions ◽  
Wind Speeds ◽  
And Performance ◽  
Lift And Drag ◽  
The Impact

Abstract Wind turbines operate in challenging environmental conditions. In hot and dusty climates, blades are constantly exposed to abrasive particles that, according to many field reports, cause significant damages to the leading edge. On the other hand, in cold climates similar effects can be caused by prolonged exposure to hail and rain. Quantifying the effects of airfoil deterioration on modern multi-MW wind turbines is crucial to correctly schedule maintenance and to forecast the potential impact on productivity. Analyzing the impact of damage on fatigue and extreme loading is also important to improve the reliability and longevity of wind turbines. In this work, a blade erosion model is developed and calibrated using computational fluid dynamics (CFD). The Danmarks Tekniske Universitet (DTU) 10 MW Reference Wind Turbine is selected as the case study, as it is representative of the future generation wind turbines. Lift and Drag polars are generated using the developed model and a CFD numerical setup. Power and torque coefficients are compared in idealized conditions at two wind speeds, i.e., the rated speed and one below it. Full aero-servo-elastic simulations of the turbine are conducted with the eroded polars using NREL's BEM-based code OpenFAST. Sixty-six 10-min simulations are performed for each stage of airfoil damage, reproducing operating conditions specified by the IEC 61400-1 power production DLC-group, including wind shear, yaw misalignment, and turbulence. Aeroelastic simulations are analyzed, showing maximum decreases in CP of about 12% as well as reductions in fatigue and extreme loading.

scholarly journals Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3318 ◽  
Author(s):  
Lixiao Cao ◽  
Zheng Qian ◽  
Hamid Zareipour ◽  
David Wood ◽  
Ehsan Mollasalehi ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wavelet Packet ◽  
Health Indicators ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Support Vector ◽  
Useful Life ◽  
Artificial Neural Network Ann

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

Monitoring Wind Turbine Vibration Based on SCADA Data

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Zijun Zhang ◽  
Andrew Kusiak
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Supervisory Control ◽  
Clustering Algorithm ◽  
Data Mining Algorithm ◽  
Vibration Data ◽  
Monitoring Model ◽  
Unsupervised Data Mining ◽  
Two Parameters

Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data.

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