Comparison Robustness of Automatic Voltage Regulator for Synchronous Generator using Neural Network and Neuro - Fuzzy controllers †


Abstract – Artificial Neural Networks (ANN) and Neuro - Fuzzy controllers can be used as intelligent controllers to control non-li¬near 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 (NARMA-L2) 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 essential part of Neuro-Fuzzy comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks are called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. The concerned neural networks and Neuro - Fuzzy controllers for AVR is examined on different models of SG and loads. The results show that the Neuro-controllers and Neuro - Fuzzy controllers have excellent responses for all SG models and loads in view point of transient response and system stability. Also it shows that the margins of robustness for Neuro - Fuzzy controller are greater than Neuro-controller.