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Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-5863-0748
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
2016 (English)In: 2016 IEEE International Energy Conference (ENERGYCON), 2016Conference paper, Published paper (Refereed)
Abstract [en]

The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

Place, publisher, year, edition, pages
2016.
Keyword [en]
Smart Grids, Condition Monitoring, Data Mining, Failure Statistics, Association Rules, Bayesian Networks
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-31710DOI: 10.1109/ENERGYCON.2016.7513929ISBN: 978-1-4673-8463-6 OAI: oai:DiVA.org:hh-31710DiVA: diva2:950978
Conference
2016 IEEE International Energy Conference (ENERGYCON), 4-8 April, Leuven, Belgium, 4-8 april, 2016
Available from: 2016-08-04 Created: 2016-08-04 Last updated: 2017-10-05Bibliographically approved
In thesis
1.
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Mashad Nemati, HassanSant´Anna, AnitaNowaczyk, Sławomir
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