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The probabilistic reliability evaluation of composite power systems is a complicated, computation intensive, and combinatorial task. As such evaluation may suffer from issues regarding high dimensionality that lead to an increased need for computational resources, MCS is often used to evaluate the reliability of power systems. In order to alleviate this burden, an analytical method known as state...
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...
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 power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a conditional state space with a higher density of failure states than the original state space...
State space pruning is a methodology that has been used to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems. This methodology improves 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 space is created. We have...
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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