Intelligent and parallel state space pruning for power system reliability analysis using MPI on a multicore platform

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 is created. A method that was previously proposed to increase the efficiency of MCS was the use of Population-based Intelligent Search (PIS) techniques including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to prune the state space. This paper improves upon these ideas by parallelizing the PIS techniques using the Open Message Passing Interface (Open MPI) in order to further improve the convergence time of MCS. The PIS algorithms and their parallel implementations are discussed and results are compared and contrasted. This method is tested using the IEEE reliability test system.