Intelligent state space pruning using multi-objective PSO for reliability analysis of composite power systems: Observations, analyses, and impacts

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. These PIS techniques encompass the field of population based metaheuristics such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and others. Most of these PIS based methods consider single objective formulations where the effect of transmission line failures on the system are not considered. As such, this work examines the impact that transmission line failures have on both MCS and PSO as used for state space pruning. A successful method for applying multi-objective PSO (MOPSO) to state space pruning is also proposed and examined. All methods are implemented and compared using the IEEE Reliability Test System.