Power system reliability assessment using intelligent state space pruning techniques: A comparative study

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 previously proposed using Population-based Intelligent Search (PIS), specifically Genetic Algorithms (GA) and Binary Particle Swarm Optimization (BPSO), to prune the state space. This paper reexamines these techniques, suggests improvements, examines the extension of these techniques to a larger test system, and extends the method to include both Repulsive Binary Particle Swarm Optimization (RBPSO) and Binary Ant Colony Optimization (BACO). These methods are tested using the single and three area IEEE Reliability Test Systems.

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