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Bradford
United Kingdom
Data Science
Electrical Engineering
Environmental Engineering
Internet Of Things
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, the total renewable energy share in the global energy mix would need to increase to 79% by 2050 [1] and wind energy is a key solution in the global long-term energy mix [2]. By 2030, wind energy will be one of the largest electricity generation sources with 24% of the total electricity needs [1]. 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 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 d.c.dao@bradford.ac.uk for more details. Formal applications can be made on the University of Bradford web site.
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.
References
[1] IRENA, “World Energy Transitions Outlook 2022: 1.5°C Pathway.” International Renewable Energy Agency, Abu Dhabi, 2022.
[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. [Online]. Available: https://ore.catapult.org.uk/?orecatapultreports=offshore-wind-operations-maintenance-9bn-year-opportunity-2030-uk-seize
[4] A. S. M. Shihavuddin et al., “Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis,” Energies, vol. 12, no. 4, Art. no. 4, Jan. 2019, doi: 10.3390/en12040676.
[5] A. Shihavuddin and X. Chen, “DTU - Drone inspection images of wind turbine,” Mendeley Data, vol. 2, Sep. 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, vol. 24, no. 11, 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. Accessed: Sep. 03, 2021. [Online]. Available: https://ore.catapult.org.uk/orecatapultreports/offshore-wind-and-hydrogen-solving-the-integration-challenge/
[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. [Online]. Available: https://ore.catapult.org.uk/?orecatapultreports=offshore-wind-operations-maintenance-9bn-year-opportunity-2030-uk-seize
[4] A. S. M. Shihavuddin et al., “Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis,” Energies, vol. 12, no. 4, Art. no. 4, Jan. 2019, doi: 10.3390/en12040676.
[5] A. Shihavuddin and X. Chen, “DTU - Drone inspection images of wind turbine,” Mendeley Data, vol. 2, Sep. 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, vol. 24, no. 11, 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. Accessed: Sep. 03, 2021. [Online]. Available: https://ore.catapult.org.uk/orecatapultreports/offshore-wind-and-hydrogen-solving-the-integration-challenge/
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