Structural health monitoring of offshore wind turbines using automated operational modal analysis

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Christof Devriendt (Author) Other; Filipe Magalhães (Author) FEUP; Wout Weijtjens (Author) Other; Gert De Sitter (Author) Other; Álvaro Cunha (Author) FEUP; Patrick Guillaume (Author) Other

Journal

Title: Structural Health Monitoring

Vol. 13 No. 6

Pages: 644-659

ISSN: 1475-9217

Publisher: SAGE

Indexing

ISI Web of Knowledge - 29 Citations

ISI Web of Science

Scopus - 74 Citations

INSPEC

Scientific classification

CORDIS: Technological sciences > Engineering > Civil engineering > Structural engineering

FOS: Engineering and technology > Civil engineering

Other information

Authenticus ID: P-009-Y60

DOI: 10.1177/1475921714556568

Abstract (EN): This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16