An approach to the characterisation of the performance of a tidal stream turbine

Matthew Allmark, Roger Grosvenor and Paul Prickett

Renewable Energy, 2017, vol. 111, issue C, 849-860

Abstract: In order to better manage and maintain deployed Tidal Stream Turbine (TST) devices their response to complicated and severe loading mechanisms must be established. To aid this process the research presented details a methodology for mapping TST operational data, taken under a variety of operating conditions, to a set of model parameters. The parameter sets were developed based on a TST rotor torque model which, as well as providing means of characterising turbine behaviour, can be used to create TST simulations with minimal computation expense. The use of the model in facilitating parameter surface mapping is demonstrated via its application to a set of rotor torque measurements made of a 1/20th scale TST during flume testing. This model is then deployed to recreate the known rotor behaviour which is compared with the original flume based measurements. This is a flexible tool that can be applied to investigate turbine performance under conditions that cannot be readily replicated using tank-based experiments. Furthermore, Computational Fluid Dynamics simulations of such conditions could be time consuming and computationally expensive. To this end, the use of the model in creating drivetrain test bed based simulations is demonstrated. The model, which can be calculated in real-time, is used to develop representative turbine simulations at high turbulence intensity levels which were not achievable during flume experimentation. The intention is to provide a test-bed for future turbine performance monitoring under more realistic, site specific conditions. The work will also support the deployment of performance surfaces in real-life turbine applications.

Keywords: Tidal stream turbine; Time-frequency methods; Rotor fault diagnosis; Motor; Generator test bed (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:111:y:2017:i:c:p:849-860

DOI: 10.1016/j.renene.2017.05.010

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