A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods

Meghdad Khazaee, Pierre Derian and Anthony Mouraud

Renewable Energy, 2022, vol. 199, issue C, 1568-1579

Abstract: In this article, a feasibility study has been carried out in order to detect structural faults in the blade by analyzing the tower vibration. A 5-MW onshore wind turbine was modeled using the NREL FAST code. The structural faults were created on the blade, while only the tower was instrumented by accelerometers and displacement meters. The wind grid was modeled in four Wind Turbulence Intensity (WTI) levels from a highly laminar to a turbulent wind flow by NREL TurbSim code. The tower vibrations were captured in the different WTIs and blade health conditions. Time Series Amplitude (TSA), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Statistical Feature Function (SFF) of the tower vibration were calculated and utilized to reveal a faulty blade effect on the tower vibrations. Eventually, a Convolutional Neural Network (CNN) classifier was developed to classify the tower vibrations collected in the blade's healthy and faulty conditions. The results showed that a defective blade has a considerable and detectable effect on the tower vibration. It is observed that blade fault could be precisely tracked and diagnosed mostly in tower Side-to-Side displacements. Also, a reverse relationship between WTI and classification accuracy was concluded.

Keywords: Wind turbine; Fault diagnosis; Structural health monitoring (SHM); Machine learning; Convolutional neural network (CNN); FAST simulation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122013829
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:199:y:2022:i:c:p:1568-1579

DOI: 10.1016/j.renene.2022.09.032

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().