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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 514608, 21 pages
http://dx.doi.org/10.1155/2014/514608
Research Article

Inverse Optimal Control with Speed Gradient for a Power Electric System Using a Neural Reduced Model

1CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. Las Aguilas, 45079 Zapopan, JAL, Mexico
2CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza La Luna, 45091 Guadalajara, JAL, Mexico

Received 5 November 2013; Revised 30 January 2014; Accepted 30 January 2014; Published 16 March 2014

Academic Editor: Hamid R. Karimi

Copyright © 2014 Alma Y. Alanis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in three different cases in the lines of transmission.