Imprecise reliability assessment of generating systems involving interval probability

Abstract

Probabilistic information about random variables describing equipment's reliability is not complete when there is a lack of statistical data about failure. The traditional reliability assessment cannot deal with the incomplete probabilistic information. Interval probability is an efficient method to address the incomplete probabilistic information. The interval value of reliability indices can reflect the degree of completeness of probabilistic information. In this study, the optimisation model of generating systems’ imprecise reliability assessment (IRA) is established and the efficient unit-adding algorithm is proposed to obtain the upper and lower bounds of reliability indices. The probability density and the expectation of reliability indices are also calculated by the Monte Carlo simulation method. In the process of IRA, massive calculations of the traditional reliability are needed, therefore the recursive convolution algorithm, which is based on the outage capacity table and has the merit of high-computation efficiency, is adopted. A case study on a revised IEEE-RTS79 system shows the rationality and equity of the presented method.