Decision support for distribution automation : data analytics for automated fault diagnosis and prognosis

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Wang, Xiaoyu and McArthur, Stephen and Strachan, Scott and Paisley, Bruce; (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). IET, GBR, pp. 1022-1026. (https://doi.org/10.1049/oap-cired.2017.0262)

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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.

ORCID iDs

Wang, Xiaoyu ORCID logoORCID: https://orcid.org/0000-0003-0239-0279, McArthur, Stephen ORCID logoORCID: https://orcid.org/0000-0003-1312-8874, Strachan, Scott ORCID logoORCID: https://orcid.org/0000-0002-2690-496X and Paisley, Bruce;
  • Item type:Book Section
    ID code:60707
    Dates:
    Date
    Event
    1 October 2017
    Published
    24 March 2017
    Accepted
    Notes:Wang, Xiaoyu; Mcarthur, Stephen; Strachan, Scott; Paisley, Bruce: 'Decision support for distribution automation: data analytics for automated fault diagnosis and prognosis', CIRED - Open Access Proceedings Journal, 2017, 2017, (1), p. 1022-1026, DOI: 10.1049/oap-cired.2017.0262 This paper is a postprint of a paper submitted to and accepted for publication in Proceedings of 24th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record will be available at the IET Digital Library.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:18 May 2017 08:35
    Last modified:12 Dec 2024 01:15
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/60707
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