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Estimation of Individual Failure Rates for Power System Components Based on Risk Functions
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. (QED Asset Management Group)ORCID iD: 0000-0002-3543-9326
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-3014-5609
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering. (QED Asset Management Group)ORCID iD: 0000-0002-2964-7233
2019 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 34, no 4, p. 1599-1607Article in journal (Refereed) Published
Abstract [en]

The failure rate is essential in power system reliability assessment and thus far, it has been commonly assumed as constant. This is a basic approach that delivers reasonable results. However, this approach neglects the heterogeneity in component populations, which has a negative impact on the accuracy of the failure rate. This paper proposes a method based on risk functions, which describes the risk behavior of condition measurements over time, to compute individual failure rates within populations. The method is applied to a population of 12 power transformers on transmission level. The computed individual failure rates depict the impact of maintenance and that power transformers with long operation times have a higher failure rate. Moreover, this paper presents a procedure based on the proposed approach to forecast failure rates. Finally, the individual failure rates are calculated over a specified prediction horizon and depicted with a 95% confidence interval.

Place, publisher, year, edition, pages
2019. Vol. 34, no 4, p. 1599-1607
Keywords [en]
failure analysis; maintenance engineering; power system reliability; power transformers; reliability theory; higher failure rate; power system components; risk functions; power system reliability assessment;power transformers;individual failure rates estimation;transmission level;Sociology;Statistics;Maintenance engineering;Power transformers;Reliability;Power system reliability;Asset management; condition monitoring; failure rate; failure rate modeling; power transformer diagnostics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255877DOI: 10.1109/TPWRD.2019.2913777ISI: 000477724800042Scopus ID: 2-s2.0-85069930869OAI: oai:DiVA.org:kth-255877DiVA, id: diva2:1342674
Note

QC 20190820

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-12-10Bibliographically approved

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Jürgensen, Jan HenningNordström, LarsHilber, Patrik
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