Decision support for distribution automation

data analytics for automated fault diagnosis and prognosis

Xiaoyu Wang, Stephen McArthur, Scott Strachan, Bruce Paisley

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

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Abstract

Distribution Automation (DA) is deployed to reduce outage times, isolate the faulted area, and rapidly restore customer supplies following network faults. Recent developments in Supervisory Control and Data Acquisition (SCADA) and intelligent DA equipment have sought to improve reliability and security of supply. The introduction of such ‘intelligent’ technologies on distribution networks, where investment in dedicated condition monitoring equipment remains difficult to justify, presents an opportunity to capture constant streams of operational data which can offer a useful insight into underlying circuit conditions if utilised and managed appropriately.

The primary function of the NOJA Pole-Mounted Auto-Recloser (PMAR) is to isolate distribution circuits from detected faults, while attempting to minimise outages due to transient faults. However, in this process the PMAR also captures current and voltage measurements that can be analysed to inform any subsequent fault diagnosis, and potentially detect the early onset of circuit degradation, and monitor and predict its progression.

This paper details the design and development of an automated decision support system for fault diagnosis and prognosis, which can detect and diagnose evolving faults by analysing PMAR data and corresponding SCADA alarm data. A knowledge based system has been developed, utilising data science and data mining techniques, to implement diagnostic and prognostic algorithms which automate the existing manual process of post fault diagnosis and anticipation, and circuit condition assessment.
Original languageEnglish
Title of host publicationProceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED)
Place of PublicationStevenage
Number of pages5
Publication statusAccepted/In press - 24 Mar 2017
Event24th International Conference and Exhibition on Electricity Distribution - Scottish Event Campus, Glasgow, United Kingdom
Duration: 12 Jun 201715 Jun 2017
http://www.cired-2017.org/

Conference

Conference24th International Conference and Exhibition on Electricity Distribution
Abbreviated titleCIRED 2017
CountryUnited Kingdom
CityGlasgow
Period12/06/1715/06/17
Internet address

Fingerprint

Failure analysis
Automation
Poles
Networks (circuits)
Outages
Data acquisition
Voltage measurement
Knowledge based systems
Condition monitoring
Electric current measurement
Decision support systems
Electric power distribution
Data mining
Degradation

Keywords

  • distribution automation
  • outage times
  • supervisory control and data acquisition
  • pole-mounted
  • auto-recloser
  • automated decision support systems
  • fault diagnosis
  • data mining

Cite this

Wang, X., McArthur, S., Strachan, S., & Paisley, B. (Accepted/In press). Decision support for distribution automation: data analytics for automated fault diagnosis and prognosis. In Proceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED) [0262] Stevenage.
Wang, Xiaoyu ; McArthur, Stephen ; Strachan, Scott ; Paisley, Bruce. / Decision support for distribution automation : data analytics for automated fault diagnosis and prognosis. Proceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED). Stevenage, 2017.
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abstract = "Distribution Automation (DA) is deployed to reduce outage times, isolate the faulted area, and rapidly restore customer supplies following network faults. Recent developments in Supervisory Control and Data Acquisition (SCADA) and intelligent DA equipment have sought to improve reliability and security of supply. The introduction of such ‘intelligent’ technologies on distribution networks, where investment in dedicated condition monitoring equipment remains difficult to justify, presents an opportunity to capture constant streams of operational data which can offer a useful insight into underlying circuit conditions if utilised and managed appropriately. The primary function of the NOJA Pole-Mounted Auto-Recloser (PMAR) is to isolate distribution circuits from detected faults, while attempting to minimise outages due to transient faults. However, in this process the PMAR also captures current and voltage measurements that can be analysed to inform any subsequent fault diagnosis, and potentially detect the early onset of circuit degradation, and monitor and predict its progression.This paper details the design and development of an automated decision support system for fault diagnosis and prognosis, which can detect and diagnose evolving faults by analysing PMAR data and corresponding SCADA alarm data. A knowledge based system has been developed, utilising data science and data mining techniques, to implement diagnostic and prognostic algorithms which automate the existing manual process of post fault diagnosis and anticipation, and circuit condition assessment.",
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Wang, X, McArthur, S, Strachan, S & Paisley, B 2017, Decision support for distribution automation: data analytics for automated fault diagnosis and prognosis. in Proceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED)., 0262, Stevenage, 24th International Conference and Exhibition on Electricity Distribution, Glasgow, United Kingdom, 12/06/17.

Decision support for distribution automation : data analytics for automated fault diagnosis and prognosis. / Wang, Xiaoyu; McArthur, Stephen; Strachan, Scott; Paisley, Bruce.

Proceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED). Stevenage, 2017. 0262.

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

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Wang X, McArthur S, Strachan S, Paisley B. Decision support for distribution automation: data analytics for automated fault diagnosis and prognosis. In Proceedings of 24th International Conference and Exhibition on Electricity Distribution (CIRED). Stevenage. 2017. 0262