Detection, prognosis and decision support tool for offshore wind turbine structures

Date

2022-11-24

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

MDPI

Department

Type

Article

ISSN

2674-032X

Format

Free to read from

Citation

Vasquez S, Verhelst J, Brijder R, Ompusunggu AP. (2022) Detection, prognosis and decision support tool for offshore wind turbine structures, Wind, Volume 2, Issue 4, November 2022, pp. 747-765

Abstract

Corrosion is the leading cause of failure for Offshore Wind Turbine (OWT) structures and it is characterized by a low probability of detection. With focus on uniform corrosion, we propose a corrosion detection and prognosis system coupled with a Decision Support Tool (DST) and a Graphical User Interface (GUI). By considering wall thickness measurements at different critical points along the wind turbine tower, the proposed corrosion detection and prognosis system—based on Kalman filtering, empirical corrosion models and reliability theory—estimates the Remaining Useful Life of the structure with regard to uniform corrosion. The DST provides a systematic approach for evaluating the results of the prognosis module together with economical information, to assess the different possible actions and their optimal timing. Focus is placed on the optimization of the decommissioning time of OWTs. The case of decommissioning is relevant as corrosion—especially in the splash zone of the tower—makes maintenance difficult and very costly, and corrosion inevitably leads to the end of life of the OWT structure. The proposed algorithms are illustrated with examples. The custom GUI facilitates the interpretation of results of the prognosis module and the economical optimization, and the interaction with the user for setting the different parameters and costs involved.

Description

Software Description

Software Language

Github

Keywords

corrosion, fault detection and prognosis, offshore wind turbine

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Supplements

Funder/s

European Union funding: 851207