RT Journal Article
T1 Fault analysis with modular neural networks
A1 Rodríguez, C.
A1 Rementería, S.
A1 Martín, J. I.
A1 Lafuente, A.
A1 Muguerza, J.
A1 Pérez, J.
AB Automatic fault diagnosis in power systems presents real challenges to computing technologies. As an alternative approach to expert systems, several neural network solutions have been proposed recently. In this paper a modular, neural network-based solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of 'toy' alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithical diagnosis systems, the neural network-based approach presented here fulfills the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behaviour of electrical equipment provides the flexibility and speed demanded by the problem. The way in which the neural system is conceived allows full scalability to real-size power systems.
SN 0142-0615
YR 1996
FD 1996-02
LK https://hdl.handle.net/11556/3642
UL https://hdl.handle.net/11556/3642
LA eng
NO Rodríguez , C , Rementería , S , Martín , J I , Lafuente , A , Muguerza , J & Pérez , J 1996 , ' Fault analysis with modular neural networks ' , International Journal of Electrical Power and Energy Systems , vol. 18 , no. 2 , pp. 99-110 . https://doi.org/10.1016/0142-0615(95)00007-0
DS TECNALIA Publications
RD 31 jul 2024