Fully-funded PhD Studentship in Autonomous Condition Monitoring of Wind Power Systems through Quantum Machine Learning

Project Description

The UK, as the global leader in offshore wind, currently has over 10GW of offshore wind capacity installed and is on track to deliver 40GW by 2030, rising to 100GW by 2050. There are ambitious targets for wind generation to be capable of powering every home in the UK by 2030. Wind power generation will thus critically contribute to the UK net carbon zero target. Offshore wind farms are becoming larger and installed in deeper seas, where wind sources are intermittent and stochastic in nature. It is crucial that the cost of energy from offshore wind farms is driven down to an economically acceptable level. A step change is therefore required to develop high-level operations and maintenance (O&M) technologies to help secure realisation of cost-of-electricity targets for offshore wind under harsh environments.

The aim of this PhD project is to develop novel and autonomous solutions to improve condition monitoring and control capabilities and ultimately to reduce O&M costs. The main objectives include:

• Develop scalable spatial-temporal models to predict wind power outputs by considering data streams in evolving and changing offshore environments,
• Explore quantum machine learning for monitoring data in the knowledge domains to diagnose and prognose faults and failures for turbines and hence the whole wind farm,
• Investigate and implement adaptive, responsive control methods, thus optimising power output whilst fulfilling grid service requirements,
• Disseminate and exploit research outputs, ensuring that the proposed solutions will benefit the energy community in both academia and industry.

Funding

This project is funded by Lancaster University. The studentship will cover UK fees plus the standard maintenance stipend (fees paid, stipend at UKRI rates, currently £17,668 p.a. tax free). The School of Engineering also has limited funding to cover Overseas fees which this award may be eligible for. The successful candidate could start in October 2023.

Qualifications and experience

• Candidates should have a relevant degree at 2.1 minimum or an equivalent overseas degree in Electrical & Electronic Engineering, Electrical Power Systems, Renewable Energy, Control Engineering, or related disciplines.
• A good background in machine learning and computer programming is desirable.
• Excellent oral and written communication skills with the ability to prepare presentations, reports, and journal papers to the highest levels of quality.
• Excellent interpersonal skills to work effectively in a team consisting of PhD students and postdoctoral researchers.
• Non-UK students are welcome to apply. Overseas applicants may be asked to provide a recognised English language qualification, depending upon their nationality and the institutions they have studied previously, with an IELTS result of minimum 6.5 and a minimum of 6.0 in each element of the test.

Informal enquiries and how to apply

For informal enquiries, please contact Dr. Xiandong Ma (xiandong.ma@lancaster.ac.uk). Candidates interested in applying should send a copy of their CV together with a personal statement/covering letter addressing their background and suitability for this project to Dr. Xiandong Ma by the closing date: 3^rd May 2023.

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