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Advanced Maintenance Management of Offshore Renewable Energy Systems

   Faculty of Engineering and Informatics

  Dr Cuong Dao  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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, more renewable energy has been added to the grid than fossil fuels and nuclear energy combined since 2013 [1]. According to GWEC [2], wind energy is a key solution in the global long-term energy mix. With continuous investments in the ongoing and future development plans, the total global offshore wind capacity is expected to increase tenfold to 270 GW by 2030 and approaching 2000 GW by 2050 [1], [2]. Offshore wind operation and maintenance (O&M), with a large and growing fleet, market is expected to reach £9 billion per year by 2030 [3]. 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 [4] 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 [5] available. This enables efficient utilisation of advanced maintenance strategies, such as condition-based maintenance [6] 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 [7], 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 analysis, reliability engineering, maintenance modelling and simulation, and. The outcomes of this project can help improve the O&M management, reduce the maintenance cost, which is vital for the future development of offshore energy systems.

Interest applicants should have research experience in renewable energy, reliability and maintenance engineering or be willing to develop research knowledge and skills in these topics. All students with background in engineering, applied statistics, or computer science are encouraged to apply.

Funding Notes

This is a self-funded PhD project; applicants will be expected to pay their own fees or have a suitable source of third-party funding. A bench fee may also apply to this project, in addition to the tuition fees. UK students may be able to apply for a Doctoral Loan from Student Finance for financial support.


1] IRENA, “World Energy Transitions Outlook: 1.5°C Pathway.” International Renewable Energy Agency, Abu Dhabi, 2021. [Online]. Available:
[2] GWEC, “GLOBAL WIND REPORT 2021.” Global Wind Energy Council, 2021.
[3] ORE Catapult, “Offshore Wind Operations & Maintenance: A £9Bn Per Year Opportunity by 2030 for the UK to Seize.” ORE Catapult, 2021.
[4] A. S. M. Shihavuddin et al., “Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis,” Energies, vol. 12, no. 4, 2019, doi: 10.3390/en12040676.
[5] A. Shihavuddin and X. Chen, “DTU - Drone inspection images of wind turbine,” Mendeley Data, vol. 2, 2018, doi: 10.17632/hd96prn3nc.2.
[6] C. D. Dao, B. Kazemtabrizi, C. J. Crabtree, and P. J. Tavner, “Integrated condition-based maintenance modelling and optimisation for offshore wind turbines,” Wind Energy, 2021, doi: 10.1002/we.2625.
[7] A. Spyroudi, D. Wallace, G. Smart, K. Stefaniak, S. Mann, and Z. Kurban, “Offshore Wind and Hydrogen: Solving the Integration Challenge.” Offshore Wind Industry Council and ORE Catapult, 2020.

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