Wavelet Energy Transmissibility Analysis for Wind Turbine Blades Fault Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Wind turbines (WT) are increasingly deployed worldwide to harvest wind power from nature, and WT blades are the most crucial components among the WT systems. WT blades are subject to non-stationary time-varying loads and the load information is usually unknown or hard to obtain. This poses great challenges to blade condition monitoring and fault detection. To avoid WT malfunctions and further economic loss, Transmissibility Functions (TF) based approaches have been developed with the purpose of precisely detecting the incipient WT blades defects. In this paper, a recently proposed Wavelet Energy TF (WETF) method which has been successfully applied to WT bearings is transferred to WT blades fault detection. This technique can remove the impacts of external varying loads, requires no excitation information, and demonstrates robustness to noise. The effectiveness of the WETF method for WT blade fault detection is validated on three naturally-damaged industrial-scale WT blades, and its superiority over the conventional Fourier TF (FTF) method is also demonstrated.
Original languageEnglish
Title of host publicationIEEE Xplore
PublisherIEEE
ISBN (Electronic)9781728148298
ISBN (Print)9781728148304
DOIs
Publication statusPublished - 25 Jun 2020
Event2020 IEEE Applied Power Electronics Conference and Exposition (APEC) - New Orleans, United States
Duration: 15 Mar 202019 Mar 2020

Conference

Conference2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Country/TerritoryUnited States
Period15/03/2019/03/20

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