Well-being analysis for composite generation and transmission systems based on pattern recognition techniques
Well-being analysis for composite generation and transmission systems based on pattern recognition techniques
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Date
2008
Authors
Armando Leite da Silva
Vladimiro Miranda
Leonidas C. de Resende
L . A. da Fonseca Manso
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Abstract
A new methodology to evaluate well-being indices for a composite generation and transmission
system, based on non-sequential Monte Carlo simulation and pattern recognition techniques,
is presented. To classify the success operating states into healthy and marginal, an
artificial neural network based on group method data handling techniques is used to capture the
patterns of these state classes, during the beginning of the simulation process. The idea is to
provide the simulation process with an intelligent memory, based on polynomial parameters, to
speed up the evaluation of the operating states. 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.