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Antoniadou I, Dervilis N, Papatheou E, et al. Aspects of structural health and condition monitoring of offshore wind turbines. Philos Trans A Math Phys Eng Sci. 2015;373(2035)doi: 10.1098/rsta.2014.0075.
Antoniadou, I., Dervilis, N., Papatheou, E., Maguire, A. E., & Worden, K. (2015). Aspects of structural health and condition monitoring of offshore wind turbines. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 373(2035), . https://doi.org/10.1098/rsta.2014.0075
Antoniadou, I, et al. "Aspects of structural health and condition monitoring of offshore wind turbines." Philosophical transactions. Series A, Mathematical, physical, and engineering sciences vol. 373,2035 (2015). doi: https://doi.org/10.1098/rsta.2014.0075
Antoniadou I, Dervilis N, Papatheou E, Maguire AE, Worden K. Aspects of structural health and condition monitoring of offshore wind turbines. Philos Trans A Math Phys Eng Sci. 2015 Feb 28;373(2035). doi: 10.1098/rsta.2014.0075. PMID: 25583864; PMCID: PMC4290406.
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Philos Trans A Math Phys Eng Sci. 2015 Feb 28;373(2035). doi: 10.1098/rsta.2014.0075.

Aspects of structural health and condition monitoring of offshore wind turbines.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

I Antoniadou, N Dervilis, E Papatheou, A E Maguire, K Worden

Affiliations

  1. Dynamics Research Group, Department of Mechanical Engineering, The University of Sheffield, Mappin St., Sheffield S1 3JD, UK [email protected].
  2. Dynamics Research Group, Department of Mechanical Engineering, The University of Sheffield, Mappin St., Sheffield S1 3JD, UK.
  3. Vattenfall Research and Development, New Renewables, The Tun Building, Holyrood Road, Edinburgh EH8 8AE, UK.

PMID: 25583864 PMCID: PMC4290406 DOI: 10.1098/rsta.2014.0075

Abstract

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

Keywords: condition monitoring; data analysis; offshore wind turbines; structural health monitoring

References

  1. Philos Trans A Math Phys Eng Sci. 2007 Feb 15;365(1851):493-514 - PubMed

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