hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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 Probability Theory and Statistics Bioinformatics (Computational Biology) Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-31710DOI: 10.1109/ENERGYCON.2016.7513929ISI: 000390822900059Scopus ID: 2-s2.0-84982836497ISBN: 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: 2017-12-01Bibliographically approved
In thesis
1. Data-Driven Methods for Reliability Evaluation of Power Cables in Smart Distribution Grids
Open this publication in new window or tab >>Data-Driven Methods for Reliability Evaluation of Power Cables in Smart Distribution Grids
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This research aims to develop data-driven methods that automatically exploit historical data in smart distribution grids for reliability evaluation, i.e., analyzing frequency of failures, and modeling components’ lifetime. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from historical data. In particular, the data is exploited for two purposes: (a) failure pattern discovery, and (b) reliability evaluation of power cables. To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose an easy-to-understand visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset. The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling, i.e., estimating the function that describes changes in the rate of failure depending on age. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data. To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the Cox proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We show that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components. The developed methods of (a) are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) are applied on data from HEM Nät.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2017. 70 p.
Series
Halmstad University Dissertations, 34
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-35147 (URN)978-91-87045-70-7 (ISBN)978-91-87045-71-4 (ISBN)
Presentation
2017-09-29, Halda, Visionen/house J, Halmstad University, Kristian IV:s väg 3, Halmstad, 10:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Note

Funding: Knowledge Foundation & HEM Nät

Available from: 2017-11-24 Created: 2017-10-04 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

fulltext(846 kB)58 downloads
File information
File name FULLTEXT02.pdfFile size 846 kBChecksum SHA-512
836cc3e8ebece481d6454a879792865de670ba5d9a30a50b390467ef0c53cc8fc1a2c4802b6f88b8379dd4bf2e950845deed24cb55de85314ad24fba363b6a56
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Mashad Nemati, HassanSant´Anna, AnitaNowaczyk, Sławomir

Search in DiVA

By author/editor
Mashad Nemati, HassanSant´Anna, AnitaNowaczyk, Sławomir
By organisation
CAISR - Center for Applied Intelligent Systems Research
Computer ScienceProbability Theory and StatisticsBioinformatics (Computational Biology)Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 72 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

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 1850 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf