Simulation of Voltage and Current Distributions in Transmission Lines Using State Variables and Exponential Approximation

ETRI Journal ◽  
2009 ◽  
Vol 31 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Panuwat Dan-Klang ◽  
Ekachai Leelarasmee
Keyword(s):  
Transmission Lines ◽  
State Variables ◽  
Exponential Approximation ◽  
Current Distributions

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

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Alma Y. Alanis ◽  
Enrique A. Lastire ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco
Keyword(s):  
Discrete Time ◽  
Transmission Lines ◽  
Neural Model ◽  
Power Grids ◽  
State Variables ◽  
Parameter Uncertainties ◽  
Load Variations ◽  
Electric System ◽  
Types Of Faults ◽  
Speed Gradient

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.

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