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Work has recently been completed that improves the computational aspects of Monte Carlo simulation (MCS) including its total computational time and iterations required for convergence through the use of a novel technique known as state space pruning. This methodology currently exists in two distinct flavors: The analytical method and a method built on Population-based Intelligent Search (PIS) techniques...
State space pruning is a methodology that has been successfully applied to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of composite power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a new state space with a higher density of failure states than the original state...
Methods have previously been developed that improve the computational efficiency and convergence of Monte Carlo simulation (MCS) when computing the reliability indices of power systems. One of these techniques works by pruning the state space in such a manner that the MCS samples a state space that has a higher density of failure states than the original state space. This paper presents a new approach...
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