Abdulrahim Thiab Humod; Yasir Thaier Haider
Abstract
Modern power systems are complex and non-linear and their operating conditions can vary over a wide range, and since neuro - fuzzy networkcan be used as intelligent controllers to ...
Read More ...
Modern power systems are complex and non-linear and their operating conditions can vary over a wide range, and since neuro - fuzzy networkcan be used as intelligent controllers to control non-linear dynamic systems through learning, which can easily accommodate the non-linearity, time dependencies, model uncertainty and external disturbances.ANeuro-Fuzzy model system is proposed as an effective neural network controller model to achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous Generator (SG) to maintain constant terminal voltage. TheconcernedNeuro-fuzzy controller for AVRis examined on different models of SG andloads. The results show that the Neuro-Fuzzy -controllers have excellent responses for all SG models and loads in the view point of transientresponse and system stability compared with optimal PID controllers tuned by practical swarm optimization.They also show that the margins of robustness for Neuro-Fuzzy -controller aregreater thanPID controller.
