go to top scroll for more


Projects: Projects for Investigator
Reference Number NIA_NPG_021
Title Holistic Fault Prediction
Status Completed
Energy Categories Other Power and Storage Technologies(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 30%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 70%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
Northern Powergrid
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 March 2018
End Date 01 March 2022
Duration ENA months
Total Grant Value £400,000
Industrial Sectors Power
Region Yorkshire & Humberside
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , Northern Powergrid (100.000%)
  Industrial Collaborator Project Contact , Northern Powergrid (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA_NPG_021
Objectives To address these issues large amounts of network data are currently available at all voltages and associated with many different types of assets. Traditionally we have looked at such data as single variables and normally when associated with the identification and diagnosis of a particular, specific and usually active fault type. Much broader analysis of network data-flows are possible. Particular types of network activity may be characteristic of developing, but not yet active, faults. Broad holistic and interactive assessment across single and multiple data-sets may give additional insight where, for instance, disturbances on the LV network when correctly interpreted with HV network information give indications of impending faults that would not be detected by looking at any single dataset. This is a programme of work which will uncover, evaluate and prototype a range of deep data analysis algorithms and techniques which could be used to provide fault anticipation functionality within a Distribution Network Operators system. The programme will include prototype software and end user case studies, and from this an appropriate commercial development and deployment strategy will be developed for the future. Practical deployment and commissioning issues will be identified to support the move to Business as Usual. The work is speculative and the underlying basis is not currently well characterised. The project will be delivered via high-end university resource thought the establishment of one or more PhD projects through the Future Power Networks & Smart Grid s Centre for Doctoral Training The most promising opportunities for this approach have not yet been identified and as such all datasets relevant to assets at any voltage level are deemed within scope. The project will: Identify suitable existing data sets and data analysis algorithms and techniques which could be used to provide fault anticipation functionality using operational and other datasets available within Northern Powergrid and/or other DNOs or external sources. This may include those related to previous LCNF and current NIA projects, e.g. Customer Led Network Revolution and Smart Data). Audit the data and monitoring systems deployed and under development at Northern Powergrid in order to support the requirements analysis and specification activities for fault anticipation. This will also provide knowledge and understanding of practical ways to access data in real-time for fault anticipation. Make recommendations for specifications for and approaches to the capture of suitable data for fault anticipation and interpretation for any network. Research and develop holisitc, multivariable data analysis algorithms that can interpret signals and their interaction and identify complex degradation modes in advance of failures, in order to predict faults and enable network interventionbefore outages can impact customers. Prototype a fault anticipation decision support system for operational engineers based on the algortihms and techniques identified above. Report on the findings and learning from the project to other DNOs and interested parties..
Abstract NULL
Publications (none)
Final Report (none)
Added to Database 14/12/22