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

Document Type

Article

Publication Date

2-15-2020

Publication Title

IEEE Sensors Journal

Volume

20

Issue

4

First Page

2023

Keywords

Blades, discrete wavelet transforms, fault detection, probability density function (PDF)

Last Page

2033

Abstract

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.

DOI

10.1109/JSEN.2019.2948997

ISSN

1530437X

E-ISSN

15581748

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