Abstract
This paper presents a neural approach for the transient stability analysis of electric power systems (EPS). The transient stability of an EPS expresses the ability of the system to preserve synchronism after sudden severe disturbances. Its analysis needs the computation of the critical clearing time (CCT), which determines the security degree of the system. The classical methods for the determination of the CCT are computation time consuming and may be not treatable in real time. A feedforward neural network trained off-line using an historical database can approximate the simulation studies to give in real time an accurate estimate of the CCT. The identified neural network can be updated using new significant data to learn more disturbance cases. Numerical simulations are presented to illustrate the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.
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