Fault Detection of the Mooring system in Floating Offshore Wind Turbines based on the Wave-excited Linear Model
Yichao Liu, Alessandro Fontanella, Ping Wu, Riccardo M.G., Ferrari, Jan-Willem van Wingerden

TL;DR
This paper presents a fault detection scheme for mooring lines in floating offshore wind turbines using a wave-excited linear model and observer-based Mahalanobis Distance analysis, effectively identifying critical faults.
Contribution
It introduces a novel fault detection method based on a wave-excited linear model and observer design for FOWTs, addressing a gap in fault detection research for mooring systems.
Findings
The linear model accurately predicts FOWT dynamics under wave excitation.
The fault detection scheme reliably identifies critical mooring line faults.
Numerical simulations confirm the method's effectiveness in fault scenarios.
Abstract
Floating Offshore Wind Turbines (FOWTs) are more prone to suffer from faults and failures than bottom-fixed counterparts due to the severe wind and wave loads typical of deep water sites. In particular, mooring line faults may lead to unacceptably high operation and maintenance costs due to the limited accessibility of FOWTs. Detecting the mooring line faults is therefore critical, but the application of Fault Detection (FD) techniques has not been investigated yet. In this paper, an FD scheme based on a wave-excited linear model is developed to detect in a reliable way critical mooring line faults occurring at the fairlead and anchor ends. To reach the goal, a linear model of the FOWT is obtained by approximating the wave radiation and incident wave forces. Based on this model, an observer is built to predict the rigid rotor and platform dynamics. The FD scheme is thus implemented by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
