Alarm processing in electrical power systems through a neuro-fuzzy approach

This work presents a methodology that combines the use of artificial neural networks and fuzzy logic for alarm processing and identification of faulted components in electrical power systems. Fuzzy relations are established and form a database employed to train artificial neural networks. The artificial neural networks inputs are alarm patterns, while each output neuron is responsible for estimating the degree of membership of a specific system component into the class of faulted components. The proposed method allows good interpretation of the results, even in the presence of difficult corrupted alarm patterns. Tests are performed with a test system and with part of a real Brazilian system.