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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...
Genetic Algorithm (GA) is emerging as a popular tool in the optimization problems of power systems. In reliability indices calculation and adequacy assessment, methods have been previously developed to use GA as the sampling tool. One of the techniques developed is to use GA as the state space pruning tool in order to truncate the state space before calculating the reliability indices. This means...
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...
Intelligent search based techniques such as genetic algorithm (GA) have been proposed to deal with reliability evaluation of complex power systems recently. In this type of methods, the guided search is carried out on a population scale trying to find all the dominant failure states, based on which different reliability indices can be calculated accordingly. However, the process may be time-consuming...
Genetic algorithm (GA) has shown its promise in dealing with reliability evaluation of complex power systems. However, it may be computationally expensive due to its stochastic search mechanism coupled with the problem complexity. Especially, when each system state needs a load flow calculation to determine its status, the reliability evaluation process may take a long time. Parallel computation is...
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