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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

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

Revista

Título: Structural Health Monitoring

Vol. 13 Nº 6

Páginas: 644-659

ISSN: 1475-9217

Editora: SAGE

Indexação

ISI Web of Knowledge - 29 Citações

ISI Web of Science

Scopus - 74 Citações

INSPEC

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia civil > Engenharia estrutural

FOS: Ciências da engenharia e tecnologias > Engenharia civil

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16