Evaluation of loss of load probability for power systems using intelligent search based state space pruning

One methodology that has been previously developed to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems is a technique known as state space pruning. This technique works by pruning the state space in such a way 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 to limiting the state space sampled when calculating reliability indices by pruning the state space through the use of Population-based Intelligent Search (PIS). The preliminary results indicate that this technique is promising to improve the convergence performance of MCS when calculating reliability indices. This is tested using an IEEE Reliability Test System at different levels.