Online Ageing Detection in Transformer Oil Insulator Insulator

   School of Computing, Engineering & the Built Environment

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project


Please select or refer to the following reference number in your application: SCEBE-23SF-Transformer-Nekahi


The sole aim of power generation is to meet the electricity needs of consumers spread across homes and industries. This generated power meets different consumers at different voltage levels, and transformers are generally the equipment designed to supply the needed voltage to consumers. Transformers are expensive essential components of high voltage (HV) stations. They have extended mean-time-to-repair (MTTR) and enormous maintenance costs. Transformer failure can amount to the shutdown of the power station, which has serious economic consequences. Other consequences could include the loss of life, substation equipment, and environmental (ecological) consequences. 

Transformer oil insulation failure has rippling consequences on the primary purpose of power generation, the safety of personnel and the work environment, and enormous economic consequences. Consequently, their volume, purity, and reliability cannot be compromised. Ageing in transformer oil is primarily evidenced by interfacial tension and acidity. The transformer ageing detection method is classified as either intrusive or non-intrusive, destructive or non-destructive, offline or online. Intrusive detection techniques make contact with the transformer oil as opposed to non-intrusive methods. Destructive methods alter (in the short or long-run) the transformer oil properties being measured as opposed to non-destructive methods. The online detection method involves live ageing detection of the transformer oil while operating as opposed to offline ageing detection, which only involves sample collection for laboratory analysis and interpretation.

Previous works have centred on the discovery and modification of ageing detection sensors for increased sensitivity , offline assessment of aged samples by directly monitoring the growth of these ABPs or indirectly monitoring the influence of these ABPs on source inputs , and chemical sensor developments for detection of several ABPs. In this project for the first time the focus will be on implementing an online ageing assessment sensor for transformer oil. Furthermore, there has been little research on IoT integration to transformer oil online ageing systems for real-time ageing detection. This work will develop a nouvelle online ageing measurement instrument for transformer oil insulation that reports real-time TAN, IFT and other characteristic values. 


This project is available as a 3 years full-time or 6 years part-time PhD study programme with expected start date of

1 October 2023,

Candidates are encouraged to contact the research supervisors for the project before applying. 

Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using our Application Process page, choosing (engineering) and their preferred intake date. 

Please send any other enquires regarding your application to:

Chemistry (6) Engineering (12)

Funding Notes

Applicants are expected to find external funding sources to cover the tuition fees and living expenses. Alumni and International students new to GCU who are self-funding are eligible for fee discounts.
Find out more on our Research Scholarships and Studentships webpage (View Website).


Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using the Application Process page, choosing a October 2023 Start.
For further information, please contact the following supervisory team members:
Director of Studies
Name: Dr. Azam Nekahi
2nd Supervisor
Name: Dr. Shahab Frokhi
3rd Supervisor
Name: Kate McAulay

Register your interest for this project

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