Research on Fault Diagnosis of Drive Train in Wind Turbine Based on EMD and LSSVM

Hui Liu; Chao Wang; Wen Jun Yan

doi:10.4028/www.scientific.net/AMR.512-515.763

Paper Titles

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On the Effect of Slotted Blades on Savonius Wind Generator Performances by CFD Analysis
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Prediction of Ice Accretion on Wind Turbine with Numerical Method
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Research of SC for Improving LVRT Capability of FSIG
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Research on Fault Diagnosis of Drive Train in Wind Turbine Based on EMD and LSSVM
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Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model
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Study on Noise Characteristics of Concentrated Wind Energy Turbine
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The Control Strategy Study of Doubly-Fed Wind Turbine Participated in Frequency Regulation
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 512-515Research on Fault Diagnosis of Drive Train in Wind...

Research on Fault Diagnosis of Drive Train in Wind Turbine Based on EMD and LSSVM

Abstract:

Fault feature extraction method based on Empirical Mode Decomposition (EMD) and fault diagnosis model based on Least Squares Support Vector Machines (LSSVM) were proposed after typical faults in drive train for wind turbines being analyzed. An experiment was designed to verify the validity of feature extraction method and the intelligent diagnosis model. The results showed that EMD can effectively extract fault characteristics of the drive train in wind turbines, the classification speed and diagnosis accuracy of LSSVM classifier based on radial basis function are better than the SVM, BPNN and other classifiers which are commonly used in practice.

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Periodical:

Advanced Materials Research (Volumes 512-515)

Pages:

763-770

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.512-515.763

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Online since:

May 2012

Authors:

Hui Liu, Chao Wang, Wen Jun Yan

Keywords:

Drive Train, Empirical Mode Decomposition (EMD), Fault Diagnosis, Least Squares Support Vector Machines (Ls-SVM)

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