An SHM View of a CFD Model of Lillgrund Wind Farm

N. Dervilis, Angus Creech, A. Eoghan Maguire, I. Antoniadou, R.J. Barthope, K. Worden

Research output: Contribution to journalArticlepeer-review


Reliability of offshore wind farms is one of the key areas for the successful implementation of these renewable power plants in the energy arena. Failure of the wind turbine (WT) in general could cause massive financial losses but especially for structures that are operating in offshore sites. Structural Health Monitoring (SHM) of WTs is essential in order to ensure not only structural safety but also avoidance of overdesign of components that could lead to economic and structural inefficiency. A preliminary analysis of a machine learning approach in the context of WT SHM is presented here; it is based on results from a Computational Fluid Dynamics (CFD) model of Lillgrund Wind farm. The analysis is based on neural network regression and is used to predict the measurement of each WT from the measurements of other WTs in the farm. Regression model error is used as an index of abnormal response.
Original languageEnglish
Pages (from-to)164-169
Number of pages6
JournalApplied Mechanics and Materials
Publication statusPublished - Jun 2014


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