Research Associate in Transformer Data Analytics

Job Description

It is a common practice to use chemical markers to predict the ageing state of transformer insulation. However, the ageing of the paper insulation is a complex phenomenon which depends on various factors such as transformer design, loading and other ageing by-products in the insulation system. Furthermore, the chemical markers generated from paper insulation further undergo a complex partitioning process with different time constants affecting their concentrations in the oil. This project will take advantage of the time series historic oil test data available from the in-service transformers, additional post-mortem data and other design and operational data, to develop a suite of data analytics tools, including statistics, artificial intelligence (AI) / machine learning (ML) and knowledge/physics-based methods, for ageing assessment. Key factors affecting the generation and the partitioning of chemical markers need to be identified and technical solutions to be sought to deal with uncertainties caused by data/lack of data, in order to develop accurate life estimation algorithms.

As part of the project team, you will be expected to:

• Develop knowledge/physics-based methods to quantify ageing and associated chemical markers.
• Using data analytics tools, study the behaviour and trends associated with different chemical markers and develop data-based models to maximise their usefulness as indications in transformer life assessment.
• Develop statistical models to estimate the lifetime of transformers in-service by utilizing the post-mortem and other available data.
• By combining knowledge, models and AI/ML data analytics methods, develop end-of-life criteria and accurate life estimation algorithms based on measured chemical markers.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status.  All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Hybrid working arrangements may be considered.

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk. Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor Zhongdong Wang & Professor Qiang Liu

Email: zhongdong.wang@manchester.ac.uk Qiang.liu@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support:

https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.