Cyclostationary Analysis of a Faulty Bearing in the Wind Turbine

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Zhiyong Ma ◽  
Yibing Liu ◽  
Dameng Wang ◽  
Wei Teng ◽  
Andrew Kusiak
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Economic Loss ◽  
Turbine Generator ◽  
Bearing Faults ◽  
Outer Race ◽  
Wind Turbine Generator ◽  
Cyclic Frequency ◽  
Fault Features

Bearing faults occur frequently in wind turbines, thus resulting in an unplanned downtime and economic loss. Vibration signal collected from a failing bearing exhibits modulation phenomenon and “cyclostationarity.” In this paper, the cyclostationary analysis is utilized to the vibration signal from the drive-end of the wind turbine generator. Fault features of the inner and outer race become visible in the frequency–cyclic frequency plane. Such fault signatures can not be produced by the traditional demodulation methods. Analysis results demonstrate effectiveness of the cyclostatonary analysis. The disassembled faulty bearing visualizes the fault.

Wind Turbine Generator Fault Detection by Wavelet-Based Multifractal Analysis

2013 ◽  
Vol 644 ◽  
pp. 346-349
Author(s):  
Chang Zheng Chen ◽  
Yu Zhang ◽  
Quan Gu ◽  
Yan Ling Gu
Keyword(s):  
Wind Turbine ◽  
Multifractal Analysis ◽  
Vibration Signal ◽  
Rotational Frequency ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Wind Turbine Generator ◽  
Fault Features ◽  
Multifractal Theory ◽  
Incipient Fault

It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the complexity of the vibration signals increases with the rotational frequency. These demonstrate the multifractal analysis is effective to extract the fault features of wind turbine generator.

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Anoop Verma ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Carbon Brushes ◽  
Critical Components ◽  
Mining Algorithms

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.

Fault Diagnosis Platform Development Based on Wavelet Transform for Wind Turbines Transmission System

2013 ◽  
Vol 333-335 ◽  
pp. 632-635
Author(s):  
Zhao Ran Hou ◽  
Wu Wang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Complex Structure ◽  
Vibration Signal ◽  
Transmission System ◽  
Measurement Unit ◽  
Diagnosis System ◽  
Fault Features

Wind turbine transmission system with complex structure and abundant fault features, also the fault features are variable, three typical fault and failure forms of rolling bearings, shafts and gears in wind turbine drive train were analyzed, also the failure mechanism and corresponding vibration signal characteristics was proposed. In the wind turbines transmission system, the vibration signal can reflect most of the fault information, as there was non-stationary signals in the vibration signals and wavelet transform for feature extraction was proposed. The whole framework of the fault diagnosis platform was constructed, the lower machine based on the measurement unit and the PC based host computer diagnosis system was designed, the diagnosis system was powerful and easily cut, which was strong help to the fault character extraction, also it was practical and available for wind turbine fault diagnosis.

scholarly journals Evaluation of wind potential for an optimum choice of wind turbine generator on the sites of Lomé, Accra, and Cotonou located in the gulf of Guinea

2016 ◽  
Vol 5 (3) ◽  
pp. 211-223 ◽  
Author(s):  
Akim Adekunlé Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Koffi-Sa Bedja
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Gulf Of Guinea ◽  
Energy Potential ◽  
Turbine Generator ◽  
Average Speed ◽  
Wind Turbine Generator ◽  
Optimum Choice ◽  
Wind Potential

This work presents the characterization and assessment of wind energy potential in annual and monthly levels of the sites of Lomé, Accra and Cotonou located in the Gulf of Guinea, and the optimal characteristics of wind turbines to be installed on these sites. Studies of characterization and the wind potential of these sites from the wind speed data collected over a period of thirteen years at a height of 10 meters above the ground, show an annual average speed of 3.52 m/s for Lomé, 3.99 m/s for Cotonou and 4.16 m/s for Accra. These studies also showed that a monthly average speed exceeding 4 m/s was observed on the sites of Cotonou and Accra during the months of February, March, April, July, August and September and during the months of July, August and September on the site of Lomé. After a series of simulation conducted using the software named PotEol that we have developed in Scilab, we have retained that the wind turbines rated speeds of ~8 to 9 m/s at the sites of Lomé and Cotonou and ~ 9 to 10 m/s on the site of Accra would be the most appropriate speeds for optimal exploitation of electric energy from wind farms at a height of 50 m above the ground.Article History: Received May 26th 2016; Received in revised form August 24th 2016; Accepted August 30th 2016; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A , Kodjo, M.K. and Bédja, K. (2016) Evaluation of Wind Potential for an Optimum Choice of Wind Turbine Generator on the Sites of Lomé, Accra, and Cotonou Located in the Gulf of Guinea. Int. Journal of Renewable Energy Development, 5(3), 211-223.http://dx.doi.org/10.14710/ijred.5.3.211-223

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