Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions

Xiaoyu Wang, Scott Strachan, John Kirkwood, Stephen McArthur

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

3 Citations (Scopus)

Abstract

Historically restoration switching strategies have been deployed to improve the system reliability by addressing effects resulting from permanent fault activity via distribution automation schemes. This paper investigates the potential role of a Distribution Automation System (DAS) in dealing with nuisance tripping where customers are often disturbed by frequently occurring short-term supply outages affecting their quality of supply. In order to mitigate these nuisance supply interruptions, this paper will focus on the development of an integrated decision support system that utilises available Supervisory Control And Data Acquisition (SCADA) alarm data and distribution network data available from pole mounted auto-reclosers (provided by a distribution network operator) to detect and diagnose pre-permanent fault activity responsible for nuisance tripping. The developed system will detect the advent of nuisance tripping on a circuit, and subsequently conduct prognostic health checks to determine the cause of the underlying circuit fault activity, responsible for the nuisance tripping, which often leads to more serious permanent faults in the future. Previous fault activity could be used to identify fault signature data patterns and trends through data mining techniques that predict potential nuisance tripping associated with network behaviour, transient activity and permanent network faults. The distribution automation scheme may be subsequently operated to mitigate the risk of nuisance tripping and so improve the level of customer service associated with network operators. This system is capable of providing `early warning' of evolving faults which in turn allows an intervention to take place to inform and assist maintenance staff to take appropriate preventative action to minimise negative effect prior to it occurring.
Original languageEnglish
Title of host publication2014 49th International Universities Power Engineering Conference (UPEC)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Print)978-1-4799-6556-4
DOIs
Publication statusPublished - 2014
Event49th International Universities Power Engineering Conference, UPEC 2014 - Cluj-Napoca, Romania
Duration: 2 Sep 20145 Sep 2014

Conference

Conference49th International Universities Power Engineering Conference, UPEC 2014
CountryRomania
CityCluj-Napoca
Period2/09/145/09/14

Fingerprint

Poles
Automation
Electric power distribution
Networks (circuits)
Decision support systems
Outages
Restoration
Data mining
Data acquisition
Health

Keywords

  • SCADA systems
  • control engineering computing
  • decision support system
  • data mining
  • power distribution faults

Cite this

Wang, X., Strachan, S., Kirkwood, J., & McArthur, S. (2014). Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions. In 2014 49th International Universities Power Engineering Conference (UPEC) (pp. 1-6). IEEE. https://doi.org/10.1109/UPEC.2014.6934653
Wang, Xiaoyu ; Strachan, Scott ; Kirkwood, John ; McArthur, Stephen. / Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions. 2014 49th International Universities Power Engineering Conference (UPEC). IEEE, 2014. pp. 1-6
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title = "Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions",
abstract = "Historically restoration switching strategies have been deployed to improve the system reliability by addressing effects resulting from permanent fault activity via distribution automation schemes. This paper investigates the potential role of a Distribution Automation System (DAS) in dealing with nuisance tripping where customers are often disturbed by frequently occurring short-term supply outages affecting their quality of supply. In order to mitigate these nuisance supply interruptions, this paper will focus on the development of an integrated decision support system that utilises available Supervisory Control And Data Acquisition (SCADA) alarm data and distribution network data available from pole mounted auto-reclosers (provided by a distribution network operator) to detect and diagnose pre-permanent fault activity responsible for nuisance tripping. The developed system will detect the advent of nuisance tripping on a circuit, and subsequently conduct prognostic health checks to determine the cause of the underlying circuit fault activity, responsible for the nuisance tripping, which often leads to more serious permanent faults in the future. Previous fault activity could be used to identify fault signature data patterns and trends through data mining techniques that predict potential nuisance tripping associated with network behaviour, transient activity and permanent network faults. The distribution automation scheme may be subsequently operated to mitigate the risk of nuisance tripping and so improve the level of customer service associated with network operators. This system is capable of providing `early warning' of evolving faults which in turn allows an intervention to take place to inform and assist maintenance staff to take appropriate preventative action to minimise negative effect prior to it occurring.",
keywords = "SCADA systems, control engineering computing, decision support system, data mining, power distribution faults",
author = "Xiaoyu Wang and Scott Strachan and John Kirkwood and Stephen McArthur",
note = "Date of Acceptance: 20/06/2014",
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Wang, X, Strachan, S, Kirkwood, J & McArthur, S 2014, Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions. in 2014 49th International Universities Power Engineering Conference (UPEC). IEEE, pp. 1-6, 49th International Universities Power Engineering Conference, UPEC 2014, Cluj-Napoca, Romania, 2/09/14. https://doi.org/10.1109/UPEC.2014.6934653

Automatic analysis of pole mounted auto-recloser data for fault prognosis to mitigate customer supply interruptions. / Wang, Xiaoyu; Strachan, Scott; Kirkwood, John; McArthur, Stephen.

2014 49th International Universities Power Engineering Conference (UPEC). IEEE, 2014. p. 1-6.

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

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AU - Kirkwood, John

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N2 - Historically restoration switching strategies have been deployed to improve the system reliability by addressing effects resulting from permanent fault activity via distribution automation schemes. This paper investigates the potential role of a Distribution Automation System (DAS) in dealing with nuisance tripping where customers are often disturbed by frequently occurring short-term supply outages affecting their quality of supply. In order to mitigate these nuisance supply interruptions, this paper will focus on the development of an integrated decision support system that utilises available Supervisory Control And Data Acquisition (SCADA) alarm data and distribution network data available from pole mounted auto-reclosers (provided by a distribution network operator) to detect and diagnose pre-permanent fault activity responsible for nuisance tripping. The developed system will detect the advent of nuisance tripping on a circuit, and subsequently conduct prognostic health checks to determine the cause of the underlying circuit fault activity, responsible for the nuisance tripping, which often leads to more serious permanent faults in the future. Previous fault activity could be used to identify fault signature data patterns and trends through data mining techniques that predict potential nuisance tripping associated with network behaviour, transient activity and permanent network faults. The distribution automation scheme may be subsequently operated to mitigate the risk of nuisance tripping and so improve the level of customer service associated with network operators. This system is capable of providing `early warning' of evolving faults which in turn allows an intervention to take place to inform and assist maintenance staff to take appropriate preventative action to minimise negative effect prior to it occurring.

AB - Historically restoration switching strategies have been deployed to improve the system reliability by addressing effects resulting from permanent fault activity via distribution automation schemes. This paper investigates the potential role of a Distribution Automation System (DAS) in dealing with nuisance tripping where customers are often disturbed by frequently occurring short-term supply outages affecting their quality of supply. In order to mitigate these nuisance supply interruptions, this paper will focus on the development of an integrated decision support system that utilises available Supervisory Control And Data Acquisition (SCADA) alarm data and distribution network data available from pole mounted auto-reclosers (provided by a distribution network operator) to detect and diagnose pre-permanent fault activity responsible for nuisance tripping. The developed system will detect the advent of nuisance tripping on a circuit, and subsequently conduct prognostic health checks to determine the cause of the underlying circuit fault activity, responsible for the nuisance tripping, which often leads to more serious permanent faults in the future. Previous fault activity could be used to identify fault signature data patterns and trends through data mining techniques that predict potential nuisance tripping associated with network behaviour, transient activity and permanent network faults. The distribution automation scheme may be subsequently operated to mitigate the risk of nuisance tripping and so improve the level of customer service associated with network operators. This system is capable of providing `early warning' of evolving faults which in turn allows an intervention to take place to inform and assist maintenance staff to take appropriate preventative action to minimise negative effect prior to it occurring.

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M3 - Conference contribution book

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