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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). (QED Asset Management Group)ORCID iD: 0000-0002-3543-9326
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. KTH Royal Institute of Technology.ORCID iD: 0000-0003-3014-5609
KTH, School of Electrical Engineering and Computer Science (EECS). KTH Royal Institute of Technology. (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: 2019-08-20Bibliographically approved

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Publisher's full textScopushttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8701475&isnumber=8771387

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Jürgensen, Jan HenningNordström, LarsHilber, Patrik
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