Research project

HEIF 2021/22 Maximising Data Driven Condition Monitoring of Marine HV Cables

Project overview

This project applied extant machine learning algorithms, developed for large-scale image datasets, on a long (3.5 year) DTS dataset (along with all relevant cable and environmental data) from three interconnectors linking Jersey to France. The specific aims of this project were to:
1. undertake dimensionality reduction and simple compression techniques to reduce computation time without significant information loss.
2. apply standard signal processing techniques to filter out negligible fluctuations.
3. with this reduced and filtered dataset, transform the problem into one of pattern recognition to enable the application of a range of techniques including: Variational Autoencoders and WaveNet.
4. use these tools to identify both spatial and temporal signals within the data and relate to known electrical (load, cable electrical) and environmental (ambient temperature; sediment thermal property; depth of cover) drivers.

Staff

Lead researchers

Professor Justin Dix

Professor in Marine Geology & Geophysics
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Other researchers

Professor Jonathon Hare BEng (Hons), PhD, FHEA, MIET

Professor

Research interests

  • My main research interests lie in the area of representation learning;
  • The long-term goal of my research is to innovate techniques that can allow machines to learn from and understand the information conveyed by data and use that information to fulfil the information needs of humans.  
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Dr George Callender

Lecturer in Electrical & Electronic Eng

Research interests

  • HV Cable Systems
  • FEA Simulations
  • Partial Discharge
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Collaborating research institutes, centres and groups

Research outputs