The offshore wind industry has grown rapidly in the past decade. Hundreds of turbines are being built in the North Sea every year. Maintenance of offshore assets is often hindered by the weather, vessel limitations and resource shortages. Deciding which maintenance tasks to carry out on the day is challenging, particularly at large wind farms, where operators have hundreds of tasks to choose from. Once the tasks have been selected, the assignment of technicians to vessels and vessels to turbines is decided, usually by human decision makers.In this thesis, methodologies for O&M decision support were developed. Formulation of the problem solved in this thesis was assisted by an offshore wind farm operator to ensure applicability of the developed solutions in the real world. Firstly, a maintenance task prioritisation approach was proposed. Secondly, a tool for optimisation of vessel routes was developed. Given a set of vessels of varying specifications and a set of turbines with a range of maintenance actions to be completed, the tool computes and visualises effective vessel routing policies.The outputs of the task prioritisation model can be used as inputs to the vessel routing optimiser to improve the quality of policies generated by the latter. This was illustrated in two case studies, which provided an in-depth analysis of the outputs of both models. The case studies have shown that considering uncertainties when planning vessel routing can yield up to 14% increase in the number of maintenance actions completed once the uncertainties have realised (compared to a policy which did not take uncertain inputs into account).Additionally, the tool was tested during a visit to an offshore wind farm operations centre. It was shown that given the same choice of maintenance tasks and vessels, the tool exactly matched the policies created by human decision makers.
|Date of Award||19 Sep 2019|
- University Of Strathclyde
|Sponsors||EPSRC (Engineering and Physical Sciences Research Council)|
|Supervisor||David McMillan (Supervisor) & Matthew Revie (Supervisor)|