Renewable energy plays an importance role in the road to net-zero greenhouse gas emissions, limiting the global temperature increase by up to 1.5°C by 2050. According to the latest report by the International Renewable Energy Agency, the total renewable energy share in the global energy mix would need to increase to 79% by 2050  and wind energy is a key solution in the global long-term energy mix . By 2030, wind energy will be one of the largest electricity generation sources with 24% of the total electricity needs . Offshore wind operation and maintenance (O&M), with a large and growing fleet, market is expected to reach £9 billion per year by 2030 . Efficient O&M management ensure reliable and economic operation of wind energy assets and that is critical for the offshore energy industry in the long-term.
Recent advances in autonomous systems such as in  bring a new challenges and opportunities in maintenance modelling and management of offshore energy. This project will investigate the applicability and impacts of advanced technologies and analyse the data of energy systems including the use of the robotic autonomous systems for inspections & maintenance of offshore wind farms and the analysis of SCADA data and autonomous inspection data  available. This enables efficient utilisation of advanced maintenance strategies, such as condition-based maintenance  and opportunistic maintenance of offshore energy systems. In addition, abundant offshore wind energy can be utilised in a hybrid energy system to produce green hydrogen from wind , which contributes to the decarbonisation of our future transport, buildings, and industry. O&M modelling and simulation of such systems will also be investigated in this project using advanced methodologies related to data analytics, reliability engineering, maintenance modelling and simulation. The outcomes of this project can help improve the O&M management and reduce the maintenance cost, which are vital for the future development of offshore renewable energy systems.
Students should have research experience in renewable energy, reliability and maintenance engineering or be willing to develop research knowledge and skills in these areas. All students with background in engineering, applied statistics, or computer science are encouraged to apply.
Enquiries and how to apply:
If you have any further enquiries, please email Dr Cuong Dao email@example.com for more details. Formal applications can be made on the University of Bradford web site.