PhD project in operations & maintenance optimization of offshore wind

Wind energy is set to be a major contributor to the decarbonisation of the energy sector. Offshore wind farms are being built without subsidy, but successful offer prices are predicted to further reduce cost. Operation & Maintenance (O&M) can contribute up to 30% of the levelized cost of energy (LCOE) of offshore wind. Making decisions for planning and scheduling of maintenance is important and plays a critical role in reducing the cost and realizing the scale of wind power.

Blade failure reduces turbine yield or in extreme cases stops turbine operation and requires replacement which is a time-consuming and costly operation involving heavy lift vessels offshore. The NWO project Holi-DOCTOR aims to investigate monitoring techniques including sound and vibration measurements and infrared measurements in a holistic framework. Timely intervention informed by effective monitoring can aid remedial action, avoiding full replacement and finally help optimize O&M schedules and improve the efficiency of wind turbine maintenance.

As a part of the Holi-DOCTOR collaborative project, this PhD work aims to investigate an O&M optimization model incorporating predictive maintenance of blades, based on advanced monitoring techniques developed by other partners to assess cost effectiveness brought by blade conditional monitoring and finally optimize O&M decision making of a wind farm.

A series of research questions need to be answered in this PhD project include what successful O&M models are to consider failure modes, failure rates and remaining life time of the blade as well as realistic repair actions and expenses collected from field test and industry partners? How such models can take into account of real surrounding logistics, supply chain and meteorological conditions? What the role of continuously monitored data would be in the maintenance decision updating ? How to eliminate the discrepancy between prediction and reality and make more effective maintenance decisions and how to quantify it? etc.

In this project, the PhD researcher would have opportunity to work with companies or industry with actual O&M data of wind farms.