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.
Highlights
Many physical systems, such as electric power grids, computer and communication networks, networked dynamical systems, transportation systems, and many others, are complex large-scale interconnected systems [1]
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 well known that electric power grids are considered as complex systems due to their interconections and number of state variables; 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. To control such large scale systems, centralized control schemes are proposed in the literature assuming available global information for the overall system
Summary
Many physical systems, such as electric power grids, computer and communication networks, networked dynamical systems, transportation systems, and many others, are complex large-scale interconnected systems [1] To control such large scale systems, centralized control schemes are proposed in the literature assuming available global information for the overall system. The paper main contributions can be stated as follows: first a RHONN is used to establish a discrete-time reduced order mathematical model for a multimachine power electric system model This neural model is used to synthesize an inverse optimal SG control law to stabilize the system and, three fault scenarios are considered in order to illustrate the applicability of the proposed scheme
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