期刊
IEEE SENSORS JOURNAL
卷 20, 期 4, 页码 2023-2033出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2019.2948997
关键词
Blades; Fault detection; Wind turbines; Wind farms; Wavelet transforms; Principal component analysis; Monitoring; Blades; fault detection; discrete wavelet transforms; probability density function (PDF)
资金
- Natural Sciences and Engineering Research Council of Canada (Canada) [860002]
- Ontario Centres of Excellence, Kruger Energy, Enbridge Inc.
- Wind Energy Institute of Canada
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.
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