Evaluation of composite reliability indices based on non-sequential Monte Carlo simulation and particle swarm optimization

Monte Carlo simulation techniques used in reliability evaluation of power system are based on sequential and non-sequential simulations. This work utilizes non-sequential state transition sampling which can be used to estimate the actual frequency index without requiring an additional enumeration procedure. A state transition sampling technique does not involve sampling of component up and down cycles and storing chronological information on the system state, as the next system state is obtained by allowing a component to undergo transition from its present state. For each sampled contingency state, a minimization load curtailment model is solved using particle swarm optimization algorithm, which gives the status of the sampled state. This approach is applied to Roy Billinton Test System (RBTS) and annualized load point indices and system indices are evaluated. Results obtained are efficient and this approach has been compared with the results of sequential simulation.