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

TL;DR

This paper introduces an imprecise probabilistic method using credal networks and the IDM to estimate the interval of conditional failure rates of transmission lines, effectively handling limited outage data and uncertainty.

Contribution

It proposes a novel imprecise probabilistic approach combining IDM and credal networks for CFR estimation with limited samples, explicitly representing uncertainty.

Findings

The method accurately estimates CFR intervals for transmission lines.

Validation on real transmission lines demonstrates effectiveness.

The approach explicitly quantifies estimation uncertainty.

Abstract

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 One significant difficulty is the lack of available outage samples, due to which the law of large numbers is no longer applicable and no convincing statistical result can be obtained.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 a single conditional variable and the failure rate of an OTL. Then a credal network is constructed to integrate the IDM estimation results corresponding to different conditional variables and infer the CFR. 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; the test results validate its effectiveness.