As a clean and expanding renewable energy source, wind energy continues to interest researchers around the globe. The current installed and projected capacity of wind power generation demonstrates the economic importance of wind technology as a sector, with strong growth expected for the foreseeable future. Indeed, the efficiency and reducing costs of wind and solar technology are driving coal-based generators out of the market. However, wind turbine blades suffer from harsh environmental conditions including heavy rain, salty seawater, storms, sand, etc. as well as the rigours of mechanical vibration that are typical of rotating machines. These effects may lead to erosion and crack formations, adversely affecting the aerodynamics of the blades. Also, Increasing the size and flexibility of wind turbines increases the requirement for advanced controllers to obtain more energy from the turbine while minimizing maintenance costs. Robust and optimal control techniques can play an important role in satisfying this need.
The turbine system involves many complex components such as the generator, blades, servo motors, and a set of sensors; each of them is subjected to different degradation and abnormalities during prolonged use. Thus, controllers that include a plant model must be robust in terms of parametric variation and un-modelled dynamics of the turbine, as well as to the effects of sensor and actuator faults on the overall wind energy system. Wind turbines are generally controlled to maximize energy extraction from the wind. This must be performed while regarding physical limitations and ensuring that loads on the wind turbine structure do not significantly reduce the lifetime of components. This reveals a trade-off in the design. Therefore, control of the wind turbines is a complex nonlinear multivariable problem. The need to optimize output in the face of these constraints motivates the use of optimal and robust model predictive control strategies for this purpose.
This PhD will focus on both an applied and theoretical study on modelling and control; developing a robust and optimal fault tolerant control strategy for wind turbines. In particular, the work will be divided into two major parts. The first part will be devoted to modelling and developing an optimal robust and predictive control method for a wind turbine and its blades to maximize energy generation and lifetime of components, whereas in the second part, the study will focus on fault identification and developing a fault-tolerant control strategy to improve the dynamic response.
Candidates are requested to submit a more detailed research proposal (of a maximum of 2000 words) on the project area as part of their application.
Research Strategy and Research Profile
Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals (https://www.un.org/sustainabledevelopment/sustainable-development-goals/
We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments. This project is part of the research activity of the Research Group – Power and Renewable Energy Systems (PRES).
How to Apply
This project is available as a 3 years full-time PhD study programme with a start date of 1st October 2019.
Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels.
Candidates are encouraged to contact the research supervisors for the project before applying. Applicants should complete the online GCU Research Application Form, stating the Project Title and Reference Number (listed above).
Please also attach to the online application, copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation.
Please send any enquiries regarding your application to: [email protected]
Applicants shortlisted for the PhD project will be contacted for an interview.
For more information on How to apply and the online application form please go to https://www.gcu.ac.uk/research/postgraduateresearchstudy/applicationprocess/
Dr. Ibrahim Beklan Kucukdemiral [email protected] https://www.gcu.ac.uk/cebe/staff/kucukdemiralibrahimbeklan/