Engineering and Technology Journal

Modern power systems are complex and non-linear and their operating can vary
over a wide range. This paper presents a linear mathematical model of the
synchronous generator to control the excitation system based on Neural Network to
simulate an Automatic Voltage Regulator. The voltage regulator is used to modify
terminal voltage for the purpose of tracking a reference voltage and comparative with
PID controller. ANN (NARMA-L2) system is proposed as an effective controller
model to achieve the desired enhancement. This model after training can be called as
(Identifier).The proposed technique is evaluated on a single machine infinite bus
under different operating conditions (no-load and full load condition) by using
MATLAB simulink software.

This paper proposes the using of artificial neural networks (ANNs') to
control the synchronous generator automatic voltage regulator (AVR), with unbalance load operating conditions. The neural network for control a nonlinear system is described and used to demonstrate the effectiveness of the neural network for control the drives with nonlinearities. In this study, performances of a simulated neural network AVR evaluated for a wide range of unbalanced loads
operating conditions. The variance factors are calculated, as an indicator of optimum operation, and their values are compared for different feedback signals and various unbalanced operating conditions. The optimum control is introduced, which gives an average variance factor in ANN controller is about 1.105%, whereas the average variance factor in traditional PI controller is about 2.035%.