Holistic Fault Prediction
Status:
Complete
Project Reference Number:
NIA_NPG_021
START DATE:
END DATE:
Project summary
Funding mechanism:
  • Network Innovation Allowance
Expenditure:
£400,000
Summary
Learnings
Documents

Historically fault location techniques have predominantly been applied to any incidents on the network that generates fault current, both intermittently occurring faults or when the fault is permanent. This applies equally to the low voltage network and the high voltage network. The primary driver has been to protect the network’s infrastructure from serious damage that can result in hazardous and unsafe conditions as a result of the continued flow of fault current. This is achieved by protection systems that interrupt fault current in as short a time as possible to limit any damage and to minimise the duration of interruptions to customers’ supplies. This is an inherently reactive approach.


In the interests of improving customer service it is beneficial to try to avoid such unplanned interruptions by anticipating when a circuit’s performance is rapidly degrading.


Ideally we can then anticipate when a fault will occur and arrange for a live line work or a planned interruption to repair a circuit before it becomes either intermittent or permanent. This will provide both an improvement to customer service and, as a result of replacing reactive with proactive interventions should also improve operational planning and efficiency.

Objectives
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 intervention before 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..