Change search
ReferencesLink to record
Permanent link

Direct link
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 (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: 978-1-4673-8463-6OAI: 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: 2016-08-10Bibliographically approved

Open Access in DiVA

fulltext(833 kB)11 downloads
File information
File name FULLTEXT01.pdfFile size 833 kBChecksum SHA-512
b6da9fb09e54e467e3cdae2cb4bf029861f0b70672eaf95940b69e64ed47f8f24e8e29a91edfddbae58a65eea73a7bf1929ece84088e46e791cd070b36f8a43d
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Mashad Nemati, HassanSant´Anna, AnitaNowaczyk, Sławomir
By organisation
CAISR - Center for Applied Intelligent Systems Research
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 11 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 54 hits
ReferencesLink to record
Permanent link

Direct link