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 artifi...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2004-04, Vol.19 (2), p.537-544
Main Authors: de Souza, J.C.S., Meza, E.M., Schilling, M.T., Do Coutto Filho, M.B.
Format: Article
Language:English
Subjects:
Alarm systems
Artificial neural networks
Brazil
Electric power generation
Fault diagnosis
Fuzzy logic
Fuzzy neural networks
Neurons
Performance evaluation
Power system faults
Power systems
System testing
Trains
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Summary: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.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2003.823205