Study the robustness of automatic voltage regulator for synchronous generator based on neural network

Abstract EN

Artificial Neural Networks (ANN) can be used as intelligent controllers to control non-linear dynamic systems through learning, which can easily accommodate the non linearity’s, time dependencies, model uncertainty and external disturbances.

Modern power systems are complex and non-linear and their operating conditions can vary over a wide range.

The Nonlinear Auto-Regressive Moving Average (NARMAL2) model system is proposed as an effective neural networks controller model to achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous Generator (SG) to maintain constant terminal voltage.

The concerned neural networks controller for AVR is examined on different models of SG and loads.

The results shows that the neuro-controllers have excellent responses for all SG models and loads in view point of transient response and system stability compared with conventional PID controllers.

Also shows that the margins of robustness for neuro-controller are greater than PID controller.