Uncertainty Analysis of Transmission Line End-of-Life Failure Model for Bulk Electric System Reliability Studies

This paper proposes a joint methodology for quantifying both the aleatory and epistemic uncertainties of the transmission line end-of-life failure model. Aleatory uncertainty accounts for the well-known probabilistic or random failures. Epistemic uncertainty modeling accounts for the lack of knowled...

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Bibliographic Details
Published in:IEEE transactions on reliability 2018-09, Vol.67 (3), p.1261-1268
Main Author: Teh, Jiashen
Format: Article
Language:English
Subjects:
Ageing
Aging
Arrhenius model
end-of-life
Failure
Failure analysis
fuzzy analysis
Fuzzy set theory
joint propagation
Life cycle engineering
loading
Power system reliability
Power transmission lines
Probabilistic logic
Probability distribution
reliability
Reliability analysis
System reliability
thermal stress
transmission line
Transmission lines
Uncertainty
Uncertainty analysis
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Summary:This paper proposes a joint methodology for quantifying both the aleatory and epistemic uncertainties of the transmission line end-of-life failure model. Aleatory uncertainty accounts for the well-known probabilistic or random failures. Epistemic uncertainty modeling accounts for the lack of knowledge and imprecision in the parameters of the failure model. Imprecision arises due to the lack of historical failure data as transmission lines have long life cycle. The studied transmission lines are also equipped with the dynamic thermal rating system. This reflects the current realistic situation where system operators are able to up-rate their networks due to the advancement of sensors, which is normally preferred over the purchase of new transmission assets. The end-of-life failure is modeled using the Arrhenius-Weibull model from the authors' previous paper. Results from this paper demonstrate that the joint analysis of both types of uncertainties are more useful, as they provide a range of reliability index values rather than a single crisp value as in the pure probabilistic approach.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2018.2837114