A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

This paper introduces a new condition monitoring approach for extracting fault signatures in wind turbine blades by utilizing the data from a real-time Supervisory Control and Data Acquisition (SCADA) system. A hybrid fault detection system based on a combination of Generalized Regression Neural Net...

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Bibliographic Details
Published in:IEEE sensors journal 2020-02, Vol.20 (4), p.2023-2033
Main Authors: Rezamand, Milad, Kordestani, Mojtaba, Carriveau, Rupp, Ting, David S.-K., Saif, Mehrdad
Format: Article
Language:English
Subjects:
Algorithms
Blades
Condition monitoring
Control systems
discrete wavelet transforms
Fault detection
Hybrid systems
Maintenance costs
Monitoring
Neural networks
Principal component analysis
Principal components analysis
probability density function (PDF)
Probability density functions
Recursive methods
Regression analysis
Statistical analysis
Supervisory control and data acquisition
Turbine blades
Wavelet analysis
Wavelet transforms
Wind farms
Wind power
Wind turbines
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Summary:This paper introduces a new condition monitoring approach for extracting fault signatures in wind turbine blades by utilizing the data from a real-time Supervisory Control and Data Acquisition (SCADA) system. A hybrid fault detection system based on a combination of Generalized Regression Neural Network Ensemble for Single Imputation (GRNN-ESI) algorithm, Principal Component Analysis (PCA), and wavelet-based Probability Density Function (PDF) approach is proposed in this work. The proposed fault detection strategy accurately detects incipient blade failures and leads to improved maintenance cost and availability of the system. Experimental test results based on data from a wind farm in southwestern Ontario, Canada, illustrate the effectiveness and high accuracy of the proposed monitoring approach.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2948997