Skip to main navigation Skip to search Skip to main content

Ilościowe miary ryzyka ukŁadów bloku energetycznego

Translated title of the contribution: Quantitative measures of risk for power unit systems

Research output: Contribution to journalArticlepeer-review

Abstract

There has been a growing interest in the analysis of the risk involved in the operation of complex technical systems and the consideration of this risk as an economic category in the financial accounts of industrial companies. The risk analysis is also used in conventional power engineering and has now become the starting point for planning basic diagnostic tests, maintenance and investment schemes. The first and most important stage of these tasks is the assessment of the level of technical risk involved in the existence and operation of such complex systems as power units. The paper presents a procedure of risk assessment for the mill installation of a power unit. The subsystems and elements of the installation are described, dangerous scenarios identified and the probability of the occurrence of the risk estimated. The quantitative measures of risk are calculated upon the characterization of the consequences of the damages. From the point of view of reliability, the mill installation is the so called: "k-out-of-n" system i.e. its is regarded as good if at least k of the n components are good. The probability of the failure of a single component of the installation, that is, of a single mill unit, was calculated by means of the fault tree method. The derived values of risk expressed in monetary terms make it possible to optimize preventive measures to avoid failures of the discussed system.

Translated title of the contributionQuantitative measures of risk for power unit systems
Original languagePolish
Pages (from-to)21-26
Number of pages6
JournalRynek Energii
Volume79
Issue number6
Publication statusPublished - 2008

ASJC Scopus subject areas

  • General Mathematics
  • General Energy

Fingerprint

Dive into the research topics of 'Quantitative measures of risk for power unit systems'. Together they form a unique fingerprint.

Cite this