State space pruning for reliability evaluation using binary particle swarm optimization

Abstract

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 space is created. A method that was previously proposed to increase the efficiency of MCS was the use of Population-based Intelligent Search (PIS), specifically Genetic Algorithms (GA), to prune the state space. This paper extends these ideas to another PIS methodology: Binary Particle Swarm Optimization (BPSO). The results of this study show that BPSO is highly effective in pruning the state space and improving the convergence of MCS. This method is tested using the IEEE reliability test system.