Applications of Markov chain Monte Carlo in large-scale system reliability evaluation

A new Monte Carlo simulation method for large-scale system reliability evaluation is presented, which is Markov chain Monte Carlo (MCMC). MCMC is a kind of dynamic Monte Carlo simulation method that introduces Markov chain in stochastic process to the Monte Carlo simulation. In this method, the Gibbs sampler utilizes a set of full conditional distributions associated with the target distribution of interest in order to define a Markov chain with an invariant distribution equal to the target distribution. The system states are sampled one by one from this Markov chain to evaluate reliability. Comparing with the classical Monte Carlo simulation method, the relativities between these states are considered, which can reflect the inherence of the system states. The results of the IEEE RTS 24-bus test system show that the proposed method is efficient in system evaluation. The proposed model improves the convergence, stability and computation speed of the reliability evaluation dramatically. Finally, the evaluation results of North-west 330 kV power system also indicate the presented method is valid and has great advantage that it applies to large-scale system evaluation.