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Published: 14 Jun 2022 1,130 views
Fulfilling the complex and numerous requirements of modern power systems is becoming increasingly more challenging, one of the primary contributors being the ageing infrastructure. This not only impacts on reliability but also creates a major dilemma for system operators who need to choose between asset life extension, and replacement and modernisation. Testing, condition monitoring and diagnostics ensure that plant and equipment meet design and operational specifications, and allow operators to manage their assets timely and effectively. A variety of condition monitoring techniques are available for high voltage equipment such as partial discharge detection, vibration measurements, and thermal imaging. Each of these techniques has its benefits and drawbacks which in many cases can affect their ability to provide a holistic and accurate assessment of an asset’s condition if used individually. Moreover, their effective utilisation requires continuous collection and analysis of large volumes of data by engineers with specialist knowledge, often using proprietary instruments and software, something that can make their adoption and implementation challenging.The research will aim to develop a holistic condition monitoring solution for power system apparatus, combining aspects of electrical and non-electrical techniques, able to provide automated condition diagnosis using artificial intelligence. It will initially involve the acquisition and analysis of data from small scale experiments in the laboratory to identify potential correlation between the different monitoring techniques as well as the capabilities of sensors and signal processing algorithms. The experience gained from the aforementioned investigation will form the basis for constructing the layers of deep neural networks to automate the condition diagnosis and reporting using machine learning. The data collected will be used for training the developed models so they can successfully identify incipient faults and categorise the condition of equipment based on severity without human intervention, and ultimately provide prognosis of asset life expectancy.
| Application Deadline | 01 Sep 2022 |
| Country to study | United Kingdom |
| School to study | University of Liverpool |
| Type | Masters |
| Sponsor | University of Liverpool |
| Gender | Men and Women |
The university will provide UKRI levels of support which, for 2022-23, cover tuition fees at the home fee rate of £4,596 and provide an annual stipend of £16,062 for 3.5 years of full-time study. International applicants will have to contribute to the higher international tuition fees.
To apply for this opportunity please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click on the 'Ready to apply? Apply online' button, to start your application.
