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Condition Monitoring of Power Electronic Components Using Data-Driven Methods


   Centre for Sustainable Engineering

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  Dr M Al-Greer, Dr Musbahu Muhammad  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Renewable energy systems, modern grid, transportation electrification, and other applications, all depends heavily on Power Electronic converters. Aging, mechanical stresses, temperature changes, all contribute to shorter system lifespans and more failures. To address these issues, several advanced condition monitoring techniques have recently been developed. However, there are several fundamental challenges in condition monitoring and health diagnostics of power electronic converters. These are related to suitability for online and real-time implementation, implementation cost, aptitude to deal with abrupt real-time changes, reducing the effect on the power converter outputs, estimation/prediction accuracy, computation complexity of the proposed algorithms, and so on. Although, in model-based condition monitoring approach, there are other challenges such as system uncertainties, environmental conditions, etc. Most recently, data-driven methods using advanced signal processing, AIs, and ML techniques have been found to provide superior solutions for addressing the aforementioned issues with cost-effective, high accuracy modelling and estimation, good tracking ability to system changes, health monitoring, and fault detection.

Therefore, this project will develop a novel condition monitoring system based on data-driven methods to monitor the State of Health (SoH) and predict the Remaining Useful Life (RUL) of power electronic converter components for diagnosis, optimal performance, and safe operation. The PhD student will conduct comprehensive study/ literature review in the area of condition monitoring for power electronic systems in order to: 1) Develop power converter models; 2) Setup experiments and collect experimental data to develop data-driven models, 3) Extreme external loading will be applied to the converter during operation to ascertain its reliability; and 4) Apply that knowledge and development of solutions to monitor and estimate the SoH and predict the RUL of the power. For system modelling and data collection, the studentship will use CAD tools such as MATLAB or Finite Element Analysis (FEA). Data collecting and system modelling could be done with cloud-based technology and Internet-of-Things (IoT) sensors.

The findings of this project should contribute in particular to the performance improvement of the converters during their service life, reliability, and safety as well as a hint for an appropriate power converter design.

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to Apply

Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191

Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

For academic enquiries, please contact Dr Maher Al-Greer; [Email Address Removed].  

For administrative enquiries before or when making your application, contact [Email Address Removed].  


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship.
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