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
Data-Driven Methods for Reliability Evaluation of Power Cables in Smart 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
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 Sciences
Identifiers
URN: urn:nbn:se:hh:diva-35147ISBN: 978-91-87045-70-7 (print)ISBN: 978-91-87045-71-4 (electronic)OAI: oai:DiVA.org:hh-35147DiVA: diva2:1147104
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: 2018-01-13Bibliographically approved
List of papers
1. Reliability Evaluation of Underground Power Cables with Probabilistic Models
Open this publication in new window or tab >>Reliability Evaluation of Underground Power Cables with Probabilistic Models
2015 (English)In: DMIN'15: The 2015 International Conference on Data Mining, 2015, 37-43 p.Conference paper, Published paper (Refereed)
Abstract [en]

Underground power cables are one of the fundamental elements in power grids, but also one of the more difficult ones to monitor. Those cables are heavily affected by ionization, as well as thermal and mechanical stresses. At the same time, both pinpointing and repairing faults is very costly and time consuming. This has caused many power distribution companies to search for ways of predicting cable failures based on available historical data.

In this paper, we investigate five different models estimating the probability of failures for in-service underground cables. In particular, we focus on a methodology for evaluating how well different models fit the historical data. In many practical cases, the amount of data available is very limited, and it is difficult to know how much confidence should one have in the goodness-of-fit results.

We use two goodness-of-fit measures, a commonly used one based on mean square error and a new one based on calculating the probability of generating the data from a given model. The corresponding results for a real data set can then be interpreted by comparing against confidence intervals obtained from synthetic data generated according to different models.

Our results show that the goodness-of-fit of several commonly used failure rate models, such as linear, piecewise linear and exponential, are virtually identical. In addition, they do not explain the data as well as a new model we introduce: piecewise constant.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-29331 (URN)
Conference
The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015
Funder
Knowledge Foundation
Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2017-10-05Bibliographically approved
2. Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
Open this publication in new window or tab >>Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
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

Keyword
Smart Grids, Condition Monitoring, Data Mining, Failure Statistics, Association Rules, Bayesian Networks
National Category
Computer Sciences Probability Theory and Statistics Bioinformatics (Computational Biology) Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-31710 (URN)10.1109/ENERGYCON.2016.7513929 (DOI)000390822900059 ()2-s2.0-84982836497 (Scopus ID)978-1-4673-8463-6 (ISBN)
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: 2018-01-10Bibliographically approved
3. Reliability Evaluation of Power Cables Considering the Restoration Characteristic
Open this publication in new window or tab >>Reliability Evaluation of Power Cables Considering the Restoration Characteristic
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents the use of the parametric proportional hazard model (PHM) for reliability ranking of power cables. Here, the Weibull PHM is used to estimate the failure rate of every individual cable based on the age of the cables and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical data. This data-driven method does not require any additional measurements on the cables. After individual failure rate estimation, the cables are ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables when considered as repairable or non-repairable components are compared. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) leads to incorrect conclusions about reliability of repairable power cables. The results show that the conclusions about different factors in PHM and cables ranking will be misleading if the cables are considered as non-repairable components. The proposed method is used to calculate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden.

Keyword
Power cable, historical data, reliability, proportional hazard model, preventive maintenance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-35470 (URN)
Note

As manuscript in thesis. Submitted.

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-24

Open Access in DiVA

fulltext(5706 kB)19 downloads
File information
File name FULLTEXT01.pdfFile size 5706 kBChecksum SHA-512
9cf6c2936cacd609efb2e91263244acec4e7fb8b9520413453311b72b4d27fd82720243f5cb8ab2d81e993e41a74e090d7f5f793f36f46222731d446f0917855
Type fulltextMimetype application/pdf

Authority records BETA

Mashad Nemati, Hassan

Search in DiVA

By author/editor
Mashad Nemati, Hassan
By organisation
CAISR - Center for Applied Intelligent Systems Research
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 19 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

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 77 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