Supervisor: Professor Stephen McArthur, University of Strathclyde
Sponsoring Company: Andritz Hydro Hammerfest
Phd Student: Grant S. Galloway
This student has graduated and the project is complete.
The desire for low-carbon technologies for power generation has sparked interest in tidal power. In contrast to wind, tidal energy is reliable and predictable, and presents island nations in particular with a large potential resource. However, the harsh deployment environment means repairs and maintenance are particularly difficult to schedule and complete. Equipment breakdown can take the turbine out of service for a prolonged period of time, removing the ability to generate electricity and money until repairs are made.
A new field of prognostics aims to move beyond identification of faults, towards predicting the future behaviour and degradation of the station and its components. With an accurate prognostic model, a monitoring system could not only identify the fault, but predict the time until failure. This would allow improved scheduling of turbine maintenance to account for spares and access off-shore.
In contrast to more established technologies, the historical data and expert knowledge that could be used to build a prognostic system do not exist for the tidal domain. Other sources of turbine information, such as system and material models, will have to be considered. The aim of this research is to develop relevant algorithms to assess component health and predict remaining lifetime reliably.