First Results from Operational Modal Analysis of a Floating Offshore Wind Turbine

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Pimenta, F (Author) FEUP; Magalhães, F. (Author) FEUP

Conference proceedings International

Title: Lecture Notes in Civil Engineering

Pages: 111-120

Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2

Milan, 30 August 2023 through 1 September 2023

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-3CJ

DOI: 10.1007/978-3-031-39109-5_12

Abstract (EN): The exploration of the wind energy available offshore is of vital importance to overcome the energy dependence on fossil fuels and, depending on the deepness of different locations, this task may only be feasible if based on floating solutions. Although the application of Operational Modal Analysis (OMA) techniques to time-series obtained from onshore wind turbines faces a significant number of difficulties, as many of its assumptions are more severely violated than in conventional civil engineering structures, it has already been shown that these can still provide an efficient and reliable way to characterise and track the dynamic properties of these structures. Floating offshore wind turbines (FOWT) face not only the same challenges related to the dynamic wind loading as the onshore cases, but also the effects of the induced platform motions from both this action and wave loading. Although OMA applications for FOWT are yet to be properly developed and tested, some numerical preliminary tests, mostly focused on the platform motions, have indicated that they may still be used to extract and characterise the complex dynamics of these structures. In this work, we apply the conventional Covariance driven Stochastic subspace identifications (SSI-COV) algorithm to the experimental data obtained from a fully operational FOWT and show that it can still be used for these structures. We also present different methodologies and modifications to the conventional data pre-processement and study their impact on the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9