Open Access
Imprecise reliability assessment of generating systems involving interval probability
Author(s) -
Qi Xianjun,
Cheng Qiao
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0874
Subject(s) - probabilistic logic , reliability (semiconductor) , computer science , reliability theory , monte carlo method , interval (graph theory) , reliability engineering , random variable , computation , mathematical optimization , algorithm , data mining , mathematics , statistics , engineering , artificial intelligence , failure rate , power (physics) , physics , quantum mechanics , combinatorics
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.