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Wind Turbine Tower State Reconstruction Method Based on the Corner Cut Recursion Algorithm.

Authors :
Liu, Hongyue
Bai, Yuxiang
Source :
Energies (19961073); Apr2024, Vol. 17 Issue 8, p1979, 26p
Publication Year :
2024

Abstract

This study introduces an innovative approach for the reconstruction of wind turbine tower states using a tangential recursion algorithm. The primary objective is to enable real-time monitoring of the operational condition of wind turbine towers. The proposed method is rooted in strain–load theory, which enables the accurate identification of tower load states. The tangential recursion algorithm is utilized to translate the strain data acquired from strategically placed sensors into reconstructed point positions. The subsequent refinement of these positions incorporates considerations of torsional loads and geometric deformations, culminating in the comprehensive and precise reconstruction of the tower's deformation behavior. Through the use of the OpenFAST V8 simulation software, a thorough analysis is conducted to investigate the load and deformation characteristics of the NREL 5 MW wind turbine tower across diverse operational scenarios. Furthermore, the load conditions corresponding to rated operating circumstances are applied to a finite element model constructed with the lumped mass method. The identification of tower load states and the comprehensive reconstruction of deformation patterns are realized through the extraction of strain data from critical points in the finite element model. The credibility and accuracy of the proposed method are rigorously evaluated by juxtaposing the identification and reconstruction outcomes with the results derived from the OpenFAST simulations and finite element analyses. Notably, the proposed method circumvents the requirement for external auxiliary calibration equipment for the tower, rendering it adaptable to a broader spectrum of operational contexts and making it consistent with unfolding trajectories in wind power advancement. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961073
Volume :
17
Issue :
8
Database :
Complementary Index
Journal :
Energies (19961073)
Publication Type :
Academic Journal
Accession number :
176904604
Full Text :
https://doi.org/10.3390/en17081979