Diagnosis of series DC arc faults - a machine learning approach
Telford, Rory and Galloway, Stuart and Stephen, Bruce and Elders, Ian (2017) Diagnosis of series DC arc faults - a machine learning approach. IEEE Transactions on Industrial Informatics, 13 (4). pp. 1598-1609. ISSN 1551-3203 (https://doi.org/10.1109/TII.2016.2633335)
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Abstract
Increasing prevalence of DC sources and loads has resulted in DC distribution being re-considered at a micro-grid level. However, in comparison to AC systems, the lack of a natural zero crossing has traditionally meant that protecting DC systems is inherently more difficult – this protection issue is compounded when attempting to diagnose and isolate fault conditions. One such condition is the series arc fault, which poses significant protection issues as their presence negates the logic of overcurrent protection philosophies. This paper proposes the IntelArc system to accurately diagnose series arc faults in DC systems. IntelArc combines time-frequency and time domain extracted features with hidden Markov models to discriminate between nominal transient behavior and arc fault behavior across a variety of operating conditions. Preliminary testing of the system is outlined with results showing that the system has the potential for accurate, generalized, diagnosis of series arc faults in DC systems.
ORCID iDs
Telford, Rory ORCID: https://orcid.org/0000-0001-6450-4302, Galloway, Stuart ORCID: https://orcid.org/0000-0003-1978-993X, Stephen, Bruce ORCID: https://orcid.org/0000-0001-7502-8129 and Elders, Ian ORCID: https://orcid.org/0000-0002-6060-9235;-
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Item type: Article ID code: 58843 Dates: DateEvent31 August 2017Published29 November 2016Published Online20 November 2016AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 28 Nov 2016 10:25 Last modified: 05 Dec 2024 01:12 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/58843