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

• 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
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2948997

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.