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Reliability Evaluation of Underground Power Cables with Probabilistic Models
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-7796-5201
2015 (English)In: DMIN'15: The 2015 International Conference on Data Mining, 2015, p. 37-43Conference 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.

Place, publisher, year, edition, pages
2015. p. 37-43
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-29331OAI: oai:DiVA.org:hh-29331DiVA, id: diva2:850092
Conference
The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015
Funder
Knowledge FoundationAvailable from: 2015-08-31 Created: 2015-08-31 Last updated: 2018-03-22Bibliographically 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. p. 70
Series
Halmstad University Dissertations ; 34
National Category
Computer Sciences
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: 2018-01-13Bibliographically approved

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Mashad Nemati, HassanSant'Anna, AnitaNowaczyk, Sławomir

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CAISR - Center for Applied Intelligent Systems ResearchHalmstad Embedded and Intelligent Systems Research (EIS)Intelligent Systems´ laboratory
Electrical Engineering, Electronic Engineering, Information Engineering

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Citation style
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