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Machine Learning in Improving Offshore Wind Turbine Operation and Maintenance (O&M)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Overview

The mix of energy supply worldwide has been changed dramatically in the last few decades. In the UK, in order to tackle climate change and increasing energy consumption, there has been a clear movement from fossils towards renewable and sustainable energy sources. Wind energy, for example, accounting for 98% of Scottish electricity demand in October 2018, has established a world-class record.

Compared with onshore wind turbines, offshore wind could provide a relatively larger capacity and a lower level of noise pollution. Offshore wind energy conversion systems are more sophisticated and new methodologies are urgently required based on more advanced analytics. In light of recent developments in the wind energy sector, it is becoming extremely difficult to ignore the existence of data science, which will still be a fast growing field over the next 10 years. More specifically, it has been widely applied to wind speed/power forecasting & predictions, conversion systems optimization, and fault detection & diagnosis.

This project will first investigate the simulation of wind turbine operations based on advanced numerical methods (e.g. CFD, FEA and multi-body method, etc.). Based on the simulated load effects, responses in normal operation and extreme conditions will be solved by a coupled model of the wind turbine system, which is significant for operation and maintenance. Finally, high-frequency SCADA data will be collected for data science/mining based on principles with Python3 and Machine Learning established by various Intelligent frameworks.

If you are new to programming (mainly Python3) and have a passion to learn/practice it with real on-site data, you are welcome to apply. If you already have a basic knowledge of general computer programming (no matter Python, Fortran, Matlab, C or C++), you will get chances to strengthen your knowledge through applying the most state-of-the-art Artificial Intelligent algorithms (focusing on Machine Learning).

Eligibility Criteria

Applicants should have a strong academic background in mechanical engineering, civil engineering, electrical engineering, ocean engineering, naval architecture, mathematics or a related subject at a Master’s level, or first class BEng/BSc Honours degree, or equivalent. Applicants must be available to commence academic studies in the UK by October 2020. For further information please contact Dr Xiaolei Liu ().

How to apply

Applicants should send their application directly to Dr Xiaolei Liu; e-mail:
Applications should include:
- Cover Letter
- CV
- Degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.

Funding Notes

Funding for: UK / EU

Funding amount: The funding covers UK / EU student tuition fees and stipend (~£15009 per year) in line with University rates.

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