On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market

Alan Turnbull, Conor McKinnon, James Carrol and Alasdair McDonald
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Alan Turnbull: Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Conor McKinnon: Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
James Carrol: Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Alasdair McDonald: Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH9 3DW, UK

Energies, 2022, vol. 15, issue 9, 1-20

Abstract: Offshore wind turbine drive train technology is evolving as developers increase size, aim to maximise availability and adapt to changing electricity grid requirements. This work first of all explores offshore technology market trends observed in Europe, providing a comprehensive overview of installed and planned capacity, showing a clear shift from smaller high-speed geared machines to larger direct-drive machines. To examine the implications of this shift in technology on reliability, stop rates for direct-drive and gear-driven turbines are compared between 39 farms across Europe and South America. This showed several key similarities between configurations, with the electrical system contributing to largest amount of turbine downtime in either case. When considering overall downtime across all components, the direct-drive machine had the highest value, which could be mainly attributed to comparatively higher downtime associated with the electrical, generator and control systems. For this study, downtime related to the gearbox and generator of the gear-driven turbine was calculated at approximately half of that of the direct-drive generator downtime. Finally, from a perspective of both reliability and fault diagnostics at component level, it is important to understand the key similarities and differences that would allow lessons learned on older technology to be adapted and transferred to newer models. This work presents a framework for assessing diagnostic models published in the literature, more specifically whether a particular failure mode and required input data will transfer well between geared and direct-drive machines. Results from 35 models found in the literature shows that the most transferable diagnostic models relate to the hydraulic, pitch and yaw systems, while the least transferable models relate to the gearbox. Faults associated with the generator, shafts and bearings are failure mode specific, but generally require some level of modification to adapt features to available data.

Keywords: wind energy; offshore; reliability; fault detection; geared; direct drive; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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