An SHM View of a CFD Model of Lillgrund Wind Farm

Nikolaos Dervilis; A.C.W. Creech; A.E. Maguire; Ifigeneia Antoniadou; R.J. Barthorpe; Keith Worden

doi:10.4028/www.scientific.net/AMM.564.164

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 564An SHM View of a CFD Model of Lillgrund Wind Farm

An SHM View of a CFD Model of Lillgrund Wind Farm

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volume 564)

Pages:

164-169

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.564.164

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Cite this paper

Online since:

June 2014

Authors:

Nikolaos Dervilis*, A.C.W. Creech, A.E. Maguire, Ifigeneia Antoniadou, R.J. Barthorpe, Keith Worden

Keywords:

CFD Modelling, Neural Network (NN), Novelty Detection, Offshore Wind Turbine Farms, Regression

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* - Corresponding Author

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