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On-line static security assessment of power systems by a progressive learning neural network

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2 Author(s)
R. Napoli ; Dipartimento di Ingegneria Elettrica Ind., Politecnico di Torino, Italy ; F. Piglione

In this paper an application of artificial neural networks to the static security assessment is presented. The main feature of this approach is the on-line learning of the relationship between the system operating point and the security related variables. A clustering neural network, purposely developed for on-line learning of a continuous data flow, is employed. Two different methods, aimed respectively to contingency ranking and direct post-fault values prediction, have been devised and compared. Numerical tests, carried out on the IEEE-30 bus system by employing a simulator purposely set up, are presented and discussed

Published in:

Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean  (Volume:3 )

Date of Conference:

13-16 May 1996