Performance of three phase AI controller based UPQC to enhance power quality of hybrid RES

Day-by-day to reduce the greenhouse effects and increase the power generating capacity the dependency of renewable energy systems are increasing. Due to its uncertainty and continuously changing characteristics introducing power quality issues into the power system. The challenging task for electrical engineers is to produce quality of electrical power with less carbon emissions, especially in a weak grid systems. In this paper, built a new featured hybrid technological concept of renewable energy generating resources to reduce the greenhouse carbon emissions. For this, developed mathematical models of two renewable energy sources, proton exchange membrane fuel cell and wind energy system with a squirrel cage induction generator. To enhance the quality of power built a most promising custom power devise, an Artificial Neural Network controller based Unified Power Quality Conditioner. Further, presented performance of an Articial Neural Network Controller based unied power quality conditioner: to handle the voltage variations due to wind fluctuations, to enhance quality of power due to sags and swells.