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.

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.

Coupled Dynamic Analysis for Multi-Unit Floating Offshore Wind Turbine in Maximum Operational and Survival Conditions

2015 ◽  
Author(s):  
H. K. Jang ◽  
H. C. Kim ◽  
M. H. Kim ◽  
K. H. Kim
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Random Waves ◽  
Mooring Lines ◽  
Floating Offshore Wind Turbine ◽  
Coupled Dynamic Analysis ◽  
Floating Offshore Wind Turbines

Numerical tools for a single floating offshore wind turbine (FOWT) have been developed by a number of researchers, while the investigation of multi-unit floating offshore wind turbines (MUFOWT) has rarely been performed. Recently, a numerical simulator was developed by TAMU to analyze the coupled dynamics of MUFOWT including multi-rotor-floater-mooring coupled effects. In the present study, the behavior of MUFOWT in time domain is described through the comparison of two load cases in maximum operational and survival conditions. A semi-submersible floater with four 2MW wind turbines, moored by eight mooring lines is selected as an example. The combination of irregular random waves, steady currents and dynamic turbulent winds are applied as environmental loads. As a result, the global motion and kinetic responses of the system are assessed in time domain. Kane’s dynamic theory is employed to formulate the global coupled dynamic equation of the whole system. The coupling terms are carefully considered to address the interactions among multiple turbines. This newly developed tool will be helpful in the future to evaluate the performance of MUFOWT under diverse environmental scenarios. In the present study, the aerodynamic interactions among multiple turbines including wake/array effect are not considered due to the complexity and uncertainty.

scholarly journals Design of Supervisory Control for Speed Control of Wind Turbine using Torque-Control Method Based on Buck Converter

2020 ◽  
Vol 190 ◽  
pp. 00019
Author(s):  
Katherin Indriawati ◽  
Choirul Mufit ◽  
Andi Rahmadiansah
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Supervisory Control ◽  
Control Method ◽  
Speed Control ◽  
Buck Converter ◽  
Torque Control ◽  
Control Mode ◽  
Rotor Speed ◽  
Integral Control

The variation of wind speed causes the electric power generated by the turbine also varies. To obtain maximum power, the rotor speed of wind turbines must be optimally rated. The rotor speed can be controlled by manipulating the torque from the generator; this method is called Torque Control. In that case, a DC-DC converter is needed as the control actuator. In this study, a buck converter-based supervisory control design was performed on the Horizontal-axis wind turbines (HAWT). Supervisory control is composed of two control loops arranged in cascade, and there is a formula algorithm as the supervisory level. The primary loop uses proportional control mode with a proportional gain of 0.3, whereas in the secondary loop using proportional-integral control mode with a proportional gain of 5.2 and an integral gain of 0.1. The Supervisory control has been implemented successfully and resulted in an average increase in turbine power of 4.1 % at 5 m s–1 and 10.58 % at 6 m s–1 and 11.65 % at 7 m s–1, compared to wind turbine systems without speed control.

scholarly journals Time-Domain Modeling of Tower Shadow and Wind Shear in Wind Turbines

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Swagata Das ◽  
Neeraj Karnik ◽  
Surya Santoso
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Wind Shear ◽  
Electrical Power ◽  
Rotational Frequency ◽  
Domain Modeling ◽  
Periodic Fluctuations ◽  
Electrical Power Output ◽  
Tower Shadow

Tower shadow and wind shear contribute to periodic fluctuations in electrical power output of a wind turbine generator. The frequency of the periodic fluctuations is times the blade rotational frequency , where is the number of blades. For three-bladed wind turbines, this inherent characteristic is known as the effect. In a weak-power system, it results in voltage fluctuation or flicker at the point of common coupling of the wind turbine to the grid. The phenomenon is important to model so as to evaluate the flicker magnitude at the design level. Hence, the paper aims to develop a detailed time-domain upwind fixed speed wind turbine model which includes the turbine's aerodynamic, mechanical, electrical, as well as tower shadow and wind shear components. The model allows users to input factors such as terrain, tower height, and tower diameter to calculate the oscillations. The model can be expanded to suit studies involving variable speed wind turbines. Six case studies demonstrate how the model can be used for studying wind turbine interconnection and voltage flicker analysis. Results indicate that the model performs as expected.

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuai Luo ◽  
Hongwei Liu ◽  
Ershi Qi
Keyword(s):  
Wind Turbines ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Scanning Strategy ◽  
Data Set ◽  
Content Type ◽  
Vibration Data ◽  
Density Based Clustering ◽  
Extreme Gradient Boosting ◽  
Data Points

PurposeThe purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.Design/methodology/approachThe algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.FindingsThe first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.Originality/valueData points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

Coupling of Two Tools for the Simulation of Floating Wind Turbines

2013 ◽  
Author(s):  
Sébastien Gueydon ◽  
Koert Lindenburg ◽  
Feike Savenije
Keyword(s):  
Computer Program ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Domain Model ◽  
Offshore Wind ◽  
Floating Body ◽  
Research Centre ◽  
Floating Wind Turbine ◽  
Floating Offshore Wind Turbines

For the design of a floating wind turbine it is necessary to take the loading due to the wind, wave and current in equal consideration. The PHATAS computer program from ECN (Energy research Centre of the Netherlands) is a time-domain aero-elastic simulation program, that accounts for the complete mutual interaction of unsteady rotor aerodynamics, structural dynamics of the rotor blades and tower, and interaction with the turbine controller under influence of turbulent wind and wave loading for fixed wind turbines. The aNySIM computer program from MARIN is a multi rigid body time domain model that accounts for wave loadings, current loadings, wind loadings, floating body dynamics, mooring dynamics. The coupled computer program aNySIM / PHATAS accounts for all loadings acting on a floating wind turbine and its response whereas PHATAS can only be used for fixed wind turbines onshore and offshore. This paper reports on the dynamic coupling between PHATAS and aNySIM. As a typical case study, the controller for floating offshore wind turbines is evaluated. This new tool has been used to repeat phase IV of the Offshore Code Comparison Collaboration (OC3) within IEA Wind Task 23, regarding floating wind turbine modelling. The results of these simulations are presented in this paper.

scholarly journals Identification of wind turbine main-shaft torsional loads from high-frequency SCADA (supervisory control and data acquisition) measurements using an inverse-problem approach

2021 ◽  
Vol 6 (6) ◽  
pp. 1401-1412
Author(s):  
W. Dheelibun Remigius ◽  
Anand Natarajan
Keyword(s):  
Mathematical Model ◽  
Inverse Problem ◽  
Data Acquisition ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Supervisory Control ◽  
High Frequency ◽  
Main Shaft ◽  
Site Specific ◽  
Torsional Loads

Abstract. To assess the structural health and remaining useful life of wind turbines within wind farms, the site-specific structural response and modal parameters of the primary structures are required. In this regard, a novel inverse-problem-based methodology is proposed here to identify the dynamic quantities of the drivetrain main shaft, i.e. torsional displacement and coupled stiffness. As a model-based approach, an inverse problem of a mathematical model concerning the coupled-shaft torsional dynamics with high-frequency SCADA (supervisory control and data acquisition) measurements as input is solved. It involves Tikhonov regularisation to minimise the measurement noise and irregularities on the shaft torsional displacement obtained from measured rotor and generator speed. Subsequently, the regularised torsional displacement along with necessary SCADA measurements is used as an input to the mathematical model, and a model-based system identification method called the collage method is employed to estimate the coupled torsional stiffness. It is also demonstrated that the estimated shaft torsional displacement and coupled stiffness can be used to identify the site-specific main-shaft torsional loads. It is shown that the torsional loads estimated by the proposed methodology is in good agreement with the aeroelastic simulations of the Vestas V52 wind turbine. Upon successful verification, the proposed methodology is applied to the V52 turbine to identify the site-specific main-shaft torsional loads and damage-equivalent load. Since the proposed methodology does not require a design basis or additional measurement sensors, it can be directly applied to wind turbines within a wind farm that possess high-frequency SCADA measurements.

scholarly journals Fatigue Assessment of Moorings for Floating Offshore Wind Turbines by Advanced Spectral Analysis Methods

2021 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Vincenzo Piscopo ◽  
Antonio Scamardella ◽  
Giovanni Battista Rossi ◽  
Francesco Crenna ◽  
Marta Berardengo
Keyword(s):  
Spectral Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Offshore Wind ◽  
Stress Process ◽  
Mooring Lines ◽  
Offshore Wind Turbines ◽  
Analysis Methods ◽  
Floating Offshore Wind Turbines

The fatigue assessment of mooring lines for floating offshore wind turbines represents a challenging issue not only for the reliable design of the stationkeeping system but also for the economic impact on the installation and maintenance costs over the entire lifetime of the offshore wind farm. After a brief review about the state-of-art, the nonlinear time-domain hydrodynamic model of floating offshore wind turbines moored by chain cables is discussed. Subsequently, the assessment of the fatigue damage in the mooring lines is outlined, focusing on the combined-spectrum approach. The relevant fatigue parameters, due to the low- and wave-frequency components of the stress process, are estimated by two different methods. The former is based on the time-domain analysis of the filtered stress process time history. The latter, instead, is based on the spectral analysis of the stress process by two advanced methods, namely the Welch and Thomson ones. Subsequently, a benchmark study is performed, assuming as reference floating offshore wind turbine the OC4-DeepCWind semisubmersible platform, equipped with the 5 MW NREL wind turbine. The cumulative fatigue damage is determined for eight load conditions, including both power production and parked wind turbine situations. A comparative analysis between time-domain and spectral analysis methods is also performed. Current results clearly show that the endorsement of advanced spectral analysis methods can be helpful to improve the reliability of the fatigue life assessment of mooring lines.

Aeromechanical Analysis of Wind Turbines Using Non-Linear Harmonic Method

2019 ◽  
Author(s):  
Shine Win Naung ◽  
Mohammad Rahmati ◽  
Hamed Farokhi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Solution Method ◽  
Computational Method ◽  
Harmonic Method ◽  
Non Linear ◽  
Solution Methods ◽  
Set Up ◽  
Large Wind

Abstract In this paper, aeromechanical analysis of wind turbines is presented. The distinctive feature of this paper is the use of frequency based non-linear harmonic method which is an efficient computational method to study unsteady periodic flow and aeroleasticity of turbomachinery applications, and extensive validation of the non-linear harmonic method against conventional time domain solution methods. This paper is an extension of the authors’ previous work which analysed the aerodynamics of the MEXICO (Model Rotor Experiments In Controlled Conditions) Experiment wind turbine. Aeromechanical analysis of the MEXICO-Experiment wind turbine as well as 1.5 MW wind turbine are conducted in this study. Both conventional time domain solution method and non-linear harmonic method are used, and compared to each other for validation and verification of the non-liner harmonic method. Using the same numerical set-up for each method demonstrates the differences and capabilities of each solution method, and their computational expenses. Finally, this paper concludes with how the aeromechanical analysis of large wind turbines can be performed effectively and efficiently using the non-linear harmonic method.

scholarly journals Vibration Analysis for Fault Detection of Wind Turbine Drivetrains—A Comprehensive Investigation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1686
Author(s):  
Wei Teng ◽  
Xian Ding ◽  
Shiyao Tang ◽  
Jin Xu ◽  
Bingshuai Shi ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Vibration Analysis ◽  
Wind Farms ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Vibration Data ◽  
Challenging Tasks ◽  
Almost All

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of wind turbine drivetrains. It enables the defect location of mechanical subassemblies and health indicator construction for remaining useful life prediction, which is beneficial to reducing the operation and maintenance costs of wind farms. This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed. Several challenging tasks and their solutions in the vibration-based fault detection of wind turbine drivetrains are proposed from the perspective of practicality for wind turbines, including the fault detection of planetary subassemblies in multistage wind turbine gearboxes, fault feature extraction under nonstationary conditions, fault information enhancement techniques and health indicator construction. Numerous naturally damaged cases representing the real operational features of industrial wind turbines are given, with a discussion of the failure mechanism of defective parts in wind turbine drivetrains as well.

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