Damage Detection for Offshore Wind Turbines Subjected to Non-Stationary Ambient Excitations: A Noise-Robust Algorithm Using Partial Measurements

Ning Yang, Peng Huang, Hongning Ye, Wuhua Zeng (), Yusen Liu, Juhuan Zheng and En Lin
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Ning Yang: School of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China
Peng Huang: School of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China
Hongning Ye: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Wuhua Zeng: Key Laboratory of Engineering Material & Structure Reinforcement in Fujian Province College, Sanming University, Sanming 365004, China
Yusen Liu: College of Engineering, Ocean University of China, Qingdao 266100, China
Juhuan Zheng: School of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China
En Lin: Fuzhou Urban and Rural Construction Group Co., Ltd., Fuzhou 350007, China

Energies, 2025, vol. 18, issue 14, 1-19

Abstract: Reliable damage detection in operational offshore wind turbines (OWTs) remains challenging due to the inherent non-stationarity of environmental excitations and signal degradation from noise-contaminated partial measurements. To address these limitations, this study proposes a robust damage detection method for OWTs under non-stationary ambient excitations using partial measurements with strong noise resistance. The method is first developed for a scenario with known non-stationary ambient excitations. By reformulating the time-domain equation of motion in terms of non-stationary cross-correlation functions, structural stiffness parameters are estimated using partially measured acceleration responses through the extended Kalman filter (EKF). To account for the more common case of unknown excitations, the method is enhanced via the extended Kalman filter under unknown input (EKF-UI). This improved approach enables the simultaneous identification of the physical parameters of OWTs and unknown non-stationary ambient excitations through the data fusion of partial acceleration and displacement responses. The proposed method is validated through two numerical cases: a frame structure subjected to known non-stationary ground excitation, followed by an OWT tower under unknown non-stationary wind and wave excitations using limited measurements. The numerical results confirm the method’s capability to accurately identify structural damage even under significant noise contamination, demonstrating its practical potential for OWTs’ damage detection applications.

Keywords: wind energy; offshore wind turbines; damage detection; non-stationary ambient excitations; measurement noise; cross-correlation functions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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