Automated operational modal analysis for the monitoring of a wind turbine blade

André Tavares, Diôgo Silvestre Drapier, Péter Zoltán Csurcsia, Tim De Troyer, Wim Desmet, Konstantinos Gryllias

Onderzoeksoutput: Conference paper

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Samenvatting

Modal analysis has developed into a major technology for the study of structural dynamics in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. Operational Modal Analysis (OMA) deals with the estimation of modal parameters on vibration data measured for operational conditions, when the excitation on the structure is not measured. In this work, OMA isperformed on a wind turbine blade undergoing wind tunnel testing. These tests included different wind speeds, pitch angles and also different health conditions of the blade,where masses of different magnitude were fixed to the blade on different locations to emulate damage conditions. In order to monitor the modal parameters across multiple days of varied tests in the wind tunnel, the Polymax modal parameter estimator was implemented, coupled with an Automated Modal Analysis methodology. This methodology
included an automatic modal parameter selection technique, using a Machine Learning (ML) clustering algorithm, coupled with a modal tracking procedure which applied statistical thresholds on the modal parameters’ values. The tracking procedure searches for modes similar to the ones calculated for healthy conditions. The results show how the modal parameters of the wind turbine blade vary with the different measured conditions in the wind tunnel. Moreover, a damage detection methodology is implemented to differentiate between the healthy and damaged conditions on the blade, by leveraging an anomaly detection algorithm using the Multivariate Gaussian Distribution (MGD). This
algorithm takes as input the modal parameters calculated by the previous Automated Modal Analysis methodology and detects statistical deviations among them which could indicate the presence of damage. All steps of this work contribute to developing an automatic framework able to detect damages on a wind turbine blade, and therefore perform Structural Health Monitoring (SHM) for different operational conditions.
Originele taal-2English
Titel14th International Workshop on Structural Health Monitoring
Plaats van productieStanford, CA 94305
UitgeverijStanford University Press
Aantal pagina's11
StatusPublished - 2023
Evenement14th International Workshop on Structural Health Monitoring : IWSHM - Stanford University, Stanford, CA, United States
Duur: 12 sep 202314 sep 2023
https://iwshm2023.stanford.edu/about-iwshm-2023

Conference

Conference14th International Workshop on Structural Health Monitoring
Verkorte titelIWSHM 2023
Land/RegioUnited States
StadStanford, CA
Periode12/09/2314/09/23
Internet adres

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