Detección y diagnóstico de fallas en sistemas eléctricos de potencia combinando una red neuronal autoasociativa y una red neuronal probabilística

Power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, the presence of both continuous and discrete variables, noisy information and lak or excess of data. Due to this, the need to develop more powerful approaches combining artificial intelligence techniques has been recognized. This paper proposes a monitoring system based on the system history data composed by two phases. In the first phase it learns the normal operation behavior of the system using an autoassociative neural network (AANN) which carries out the detection process. In the second phase the final diagnosis is given using a probabilistic neural