Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks

Abstract

This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on non-sequential Monte-Carlo simulation and artificial neural network concepts. Artificial neural network (ANN) techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e. loss of load probability, frequency, duration and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96 and to a configuration of the Brazilian South-Southeastern System.