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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, 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.

Place, publisher, year, edition, pages
2015. 37-43 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-29331OAI: oai:DiVA.org:hh-29331DiVA: diva2:850092
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: 2015-09-29Bibliographically 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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CiteExportLink to record
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Citation style
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