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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

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

Wang, Xiaoyu and Strachan, Scott and Kirkwood, John and McArthur, Stephen (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. ISBN 978-1-4799-6556-4

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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.