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The estimation of the conditional failure rate (CFR) of an overhead transmission line (OTL) is essential for power system operational reliability assessment. It is hard to predict the CFR precisely, although great efforts have been made to improve the estimation accuracy. One significant difficulty is the lack of available outage samples, due to which the law of large numbers is no longer applicable and no convincing statistical result can be obtained. To address this problem, in this paper, a novel imprecise probabilistic approach is proposed to estimate the CFR of an OTL. The imprecise Dirichlet model (IDM) is applied to establish the imprecise probabilistic relation between an operational condition and the OTL failure. Then a credal network is constructed to integrate the IDM estimation results corresponding to various operational conditions and infer the CFR of the OTL. Instead of providing a single-valued estimation result, the proposed approach predicts the possible interval of the CFR in order to explicitly indicate the uncertainty of the estimation and more objectively represent the available knowledge. The proposed approach is illustrated by estimating the CFRs of two LGJ-300 transmission lines located in the same region, and it is also compared with the existing approaches by using data generated from a virtual OTL. Test results indicate that the proposed approach can obtain much tighter and more reasonable CFR intervals compared with the contrast approaches.

Interval Estimation for Conditional Failure Rates of Transmission Lines With Limited Samples