Automatic analysis of Pole Mounted Auto-Recloser data for fault diagnosis and prognosis

X. Wang, S. M. Strachan, S. D. J. McArthur, J. D. Kirkwood

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
162 Downloads (Pure)

Abstract

Fault diagnosis is a key part of a control and protection engineer’s role to ensure the effective and stable performance of electrical power networks. One challenge is to support the analysis and application of expert judgement to the, often, large data sets generated. To assist engineers with this task and improve network reliability, this research focuses on analysing previous fault activity in order to obtain an early-warning report to assist fault diagnosis and fault prognosis.

This paper details the design of an integrated system with a fault diagnosis algorithm utilising available Supervisory Control And Data Acquisition (SCADA) alarm data and 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) (provided by a leading UK network operator). The developed system will be capable of diagnosing the nature of a circuit’s previous fault activity, underlying circuit activity and evolving fault activity and the risk of future fault activity. This will provide prognostic decision support for network operators and maintenance staff.
Original languageEnglish
Title of host publication2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
Place of PublicationPiscataway
PublisherIEEE
Number of pages6
ISBN (Print)9781509001903
DOIs
Publication statusPublished - 12 Nov 2015
Event18th Intelligent Systems Applications to Power Systems (ISAP 2015) - Porto, Portugal
Duration: 11 Sep 201517 Sep 2015

Conference

Conference18th Intelligent Systems Applications to Power Systems (ISAP 2015)
CountryPortugal
CityPorto
Period11/09/1517/09/15

Keywords

  • decision support
  • distribution automation
  • distribution network data
  • fault activity
  • fault diagnosis
  • SCADA alar data

Fingerprint Dive into the research topics of 'Automatic analysis of Pole Mounted Auto-Recloser data for fault diagnosis and prognosis'. Together they form a unique fingerprint.

  • Cite this

    Wang, X., Strachan, S. M., McArthur, S. D. J., & Kirkwood, J. D. (2015). Automatic analysis of Pole Mounted Auto-Recloser data for fault diagnosis and prognosis. In 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015 IEEE. https://doi.org/10.1109/ISAP.2015.7325519