Reliability and cost optimization of power electronic devices considering the component failure rate uncertainty

Dialynas, EN; Zafiropoulos, EP

dc.contributor.author	Dialynas, EN	en
dc.contributor.author	Zafiropoulos, EP	en
dc.date.accessioned	2014-03-01T02:42:20Z
dc.date.available	2014-03-01T02:42:20Z
dc.date.issued	2003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30948
dc.subject	Failure rate uncertainty	en
dc.subject	Latin hypercube sampling method	en
dc.subject	Monte Carlo simulation	en
dc.subject	Optimization methods	en
dc.subject	Simulated annealing algorithm	en
dc.subject	System reliability	en
dc.subject.other	Failure rate	en
dc.subject.other	Latin hypercube sampling	en
dc.subject.other	Monte Carlo Simulation	en
dc.subject.other	Optimization method	en
dc.subject.other	Simulated annealing algorithms	en
dc.subject.other	System reliability	en
dc.subject.other	Electric power systems	en
dc.subject.other	Failure analysis	en
dc.subject.other	Optimization	en
dc.subject.other	Probability distributions	en
dc.subject.other	Monte Carlo methods	en
dc.title	Reliability and cost optimization of power electronic devices considering the component failure rate uncertainty	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/PTC.2003.1304733	en
heal.identifier.secondary	http://dx.doi.org/10.1109/PTC.2003.1304733	en
heal.identifier.secondary	1304733	en
heal.publicationDate	2003	en
heal.abstract	The objective of this paper is to present an efficient methodology to obtain the optimal system structure for power electronic devices using various component types, while considering the constraints on reliability and cost. The component failure rate uncertainty is taken under consideration and it is modeled with two alternative probability distribution functions. The Latin Hypercube Sampling method is used to simulate the probability distributions and the efficiency of this stratified sampling method is compared with the typical Monte Carlo analysis method. The optimization methodology being used was the simulated annealing algorithm because of its flexibility to be applied in various system types with various constraints and its efficiency in computational time. The developed methodology was applied to a power electronic device and the results were compared with the results of the complete enumeration of the solution space. The stochastic nature of the top solutions was sampled extensively and the robustness of the optimization methodology was demonstrated. Finally, a typical power electronic device is used as a case study and the obtained results are presented. © 2003 IEEE.	en
heal.journalName	2003 IEEE Bologna PowerTech - Conference Proceedings	en
dc.identifier.doi	10.1109/PTC.2003.1304733	en
dc.identifier.volume	4	en
dc.identifier.spage	275	en
dc.identifier.epage	281	en